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The Elephant In The Music Room

on April 04, 2014, 3:39pm
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Component is a section of Aux.Out. for one-off pieces, special editorials, and lost orphans of the music discussion. Today, another cultural landslide creates the biggest Aux.Out. ever on music discovery and the services that help us navigate our musical terrain.

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There’s an Elephant in the Music Room.

You may not be able to see it, but it’s there.

A few people actually see the elephant; a few more can’t see it but have noticed that something incredibly huge is taking up a lot of space in the room; yet, most people haven’t even noticed that the room is getting smaller – or that there’s this humongous, pissed-off elephant just sitting there, scowling at them.

Oh. Nobody really wants to talk about it, either.

Instead, we hear about lots of divisive discussions between musicians, labels, tech start-ups, writers, bloggers, legislators, and music advocacy groups over issues like streaming royalty rates, copyright infringement, file sharing, fair use, declines in record sales and digital downloads, termination rights, pay-for-play, venues taking larger cuts out of touring bands’ payouts, legislation to force radio stations to pay their fair share for music use on-air, and so on…

But as those battle lines get further entrenched and fortified, the damned elephant just keeps getting bigger; and unless we all deal with this elephant – pretty quickly, too – none of those problems are going to matter.

It’s a freakin’ huge elephant, too; hell, just look at the amount of real estate it covers: as of December 2013, Spotify claimed 20 million songs; in September 2012, iTunes claimed to have over 26 million songs; and in November 2013, Deezer (a service not yet in the U.S. but available in 150 more countries than Spotify) announced a library of over 30 million songs. A more staggering number comes from The Echo Nest, a leading music intelligence company: at the time of this writing, they claim to have identified well over 35 million known songs by nearly 2.7 million artists that have now generated in excess of 1.168 trillion data points. (If you have nothing better to do, you can actually go to their site and watch those numbers grow by the second…)

Image (1) spotify.png for post 342393

And yet, the most astounding figure to date appeared in the December 23rd announcement that the Tribune Company had agreed to purchase Gracenote from Sony for $170 million; hidden within their PR boilerplate was the statement that Gracenote – which started in 1998 as CDDB and now provides music identification & recommender systems for clients like Apple, Google, and Amazon – now tracks over 180 million songs.

Meanwhile, the growth in recorded music is expanding exponentially due to order-of-magnitude changes in recording technology, lowering the nut required for musicians to produce and distribute their music; and musicians are taking advantage of those changes at an astounding rate. Soundcloud just reported that people are uploading 12 hours of new material every single minute…

And yet, realistically speaking, there’s no way to accurately quantify how much music is getting released right now, nor how many musicians there really are; and if you start to include the boatloads of older catalog materials and artists that have never been released in a file-based format, it’s nearly impossible to get an accurate grasp of how much music really exists out in the wild.

But wait: that massive mountain of music isn’t the Elephant. That’s just the real estate sitting under its shadow.

Now, even if we apply Sturgeon’s Law (90% of everything is crap) to just those middle-ground Echo Nest numbers, that would mean there’s at least 3.5 million songs by 270,000 artists that have generated nearly 117 billion data points of not-crappy music, and defining crap is often just a matter of taste…

Speaking of taste definition, each day your average, dedicated music writer is besieged by hundreds of promotional e-mails/press releases/one-sheets practically begging the writer to pay attention to a new release, all while the writer struggles to find an outlet that will accept their article pitches from an ever-diminishing number of outlets – outlets that are themselves struggling to reach eyeballs in hopes of increasing advertising revenues and must therefore tighten their coverage to articles that will draw the most eyeballs: that is, what they believe will be popular to their readership.

A cursory scan from opposite ends of the music writing spectrum reveals that Pitchfork published roughly 1,300 reviews over the last year (not including features or aggregate columns), while a mainstream media outlet like the New York Times featured weekly playlists of five or six non-classical releases and three larger reviews per week – roughly around 450 to 500 reviews.

So… there’s all that music out there, just waiting for a set of ears to hear it, and yet all you ever read or hear about are maybe a few thousand releases?

Say hello to The Music Room Elephant.

Let’s call it “Lack of Discovery.”

Wait – that’s a terrible name for an elephant…

And it doesn’t sound very threatening, does it? You might even find yourself thinking, “Shit, that’s the way it’s always been!”

Think again.

Match that mind-boggling volume of available music with staggering shifts in technology and listener habits, and Lack of Discovery rears its ugly head as an alarming and incredibly complex set of problems with potentially catastrophic ramifications for most musicians, and for the recorded music industry itself.

And there are no easy answers.

Why Discovery Matters

You’ve probably noticed the heightened use of the term “discovery” within just the last few months; and now that Beats Music has gone live with its “curated by trusted sources” discovery system – and with YouTube due to debut its own dedicated music service and Deezer expected to open up shop in the U.S. later this year – the discussion of music discovery and recommendation systems is about to get a hell of a lot louder.

So, really, is discovery that important?

From the business side of things, you need go no further for an answer than to look at the March 6th pre-emptive purchase of The Echo Nest by Spotify. As reported by Ben Sisario of The New York Times, this deal not only gives Spotify a programming advantage over its competition, but it also seems “to set up a possible conflict for The Echo Nest’s other clients, which include some of Spotify’s competitors” – like Rdio, Deezer, iHeartRadio, SiriusXM, Microsoft’s XBox Music, and Rhapsody – potentially depriving each of them of their primary source for discovery and recommendation data. According to the Times report, The Echo Nest’s CEO Jim Lucchese said his company “would honor its current contracts with these services, but gave no further details.” (Later in this article, you’ll see why this acquisition matters so deeply.)

What’s more interesting: back in November, Spotify raised $250 million in new funding, pegging its total funding since its formation in 2006 at $538 million; if Techcrunch is correct in reporting that Spotify paid $100 million for The Echo Nest – even if 90% of that is in Spotify equity – that’s still an enormous outlay for a company that may have a total valuation north of $4 billion… but has yet to post a profit.

Image (53) rdio.png for post 268816

Another measure of discovery’s value to business interests: on January 15th – the day before Spotify & Rdio preemptively announced that they’re now really really free in their attempts to blunt Beats Music’s initial PR push (now the only advantage in subscribing to either Spotify or Rdio is killing the ads in your streams) – Pandora quietly announced that it had added a recommendations function to the engine that runs its limited library of a little over 1 million songs; within 36 hours, Pandora’s stock price spiked up nearly 5% – reaching a market capitalization of $6.7 billion. (To give you some perspective, Universal Music Group, the world’s largest record label group, generated revenues just a little south of $6 billion in 2012.)

These services and their investors see discovery’s effect on user engagement – and more importantly, keeping users engaged – as absolutely vital to the long-term health of their business models; and if you go back though the last six months of music-service press announcements, read them in chronological order and parse them carefully, you might pick up on a sense of increased urgency for these services to emphasize their discovery features.

For musicians, how can discovery be a more important problem than the issues of low royalty rates paid for music streaming, or the claims that streaming is cannibalizing sales?

Well, using simple horse-in-front-of-cart logic, how are you going to collect royalties, or sell your music, or even have a remote chance at a sustainable life as a musician if nobody even knows you exist? Faced with the current overwhelming abundance of music, unless your potential listener has even the slightest clue to look for you, their odds of discovering your music is similar to your odds of plucking a single perfect snowflake out of a raging blizzard.

And if you’re an established artist and you think discovery won’t affect you, then you might be overlooking the long-term impact that this lack of discovery will have on the overall ecosystem of recorded music.

As a listener, discovery could become the deciding factor of whether you stay with downloads or physical product, move to a free streaming system, adopt a subscription model, dump a service entirely, or even give up on looking for anything new to listen to – all because of what or how something is recommended to you.

In his January 3rd essay, The Wall Street Journal’s John Jurgensen writes about facing this abundance of music without some form of easy and meaningful recommendation as “one reason why we fall back on the same stuff we’ve been listening to since senior year in high school,” and even coining a great new term for it: “search-bar paralysis.” Basically speaking, you are overwhelmed by choice… and yet, with so many choices available to you, you have no way of differentiating the signal from the noise. So, in the end, you don’t choose anything at all.

If what you’re listening to is what you’ve always listened to, you are much less likely to plunk down money for a new album or, in the case of streaming services, a subscription; and if you listen to a free version of a service with a narrow spectrum of choice – leaving the vast number of musicians out in the cold, looking in – and are subjected to an ever-spiraling number of ads to make up for that service’s lack of subscription revenue, then you might as well be listening to radio.

And coincidentally, the market that many of these outlets and services now seem to be targeting just happens to be the same turf mainstream radio lives within – a decision that may well be their biggest mistake. (More about that later, too.)

Yet bad recommendations can often be more problematic than search-bar paralysis; in the three months we’ve been working up this article, we’ve seen dozens upon dozens of remarkable tweets about problematic service recommendations – for example, this classic from Stephen T. Erlewine, senior editor of All Music Guide:

Erlewine

Or this one by writer Michele Catalano:

Catelano

Clearly, there are some big problems facing music discovery and recommendation systems; but now, if it’s so damned important, how do we fix “discovery?”

But That’s the Way It’s Always Been…

If we want to tackle the problems of “discovery,” we need to clear away some long-standing preconceptions and recognize that the technological shift occurring over the last several decades represents something much more than a change of distribution formats…

So, let’s take a very brief look at music’s history as a way to gain some perspective. (Don’t worry, history-phobes. This all ties together in the end. Besides, a little history is good for you. Don’t forget to eat your peas.)

Although phonographic reproduction was invented in the late 19th century – and even at that time, a vigorous format battle was being waged between vertically grooved cylinders and laterally grooved discs – the era of recorded music didn’t really get under way until around 1901 with the introduction of a 10” disc that could play back a whopping 3 minutes of music recorded “acoustically”; i.e., a large horn collected sound, channeled it to a diaphragm that in turn vibrated a needle that etched those vibrations into a solid, wax disc spinning beneath it, thus cutting the master recording directly to the disc. (Now that’s seriously analog audio.)

So, for the sake of argument, let’s use 1901 as the true advent of The Era of Recorded Music: this means the primary method we use for listening to music – recorded audio – is a little over 110 years old.

Pretty long time, eh?

With hard evidence provided within a report presented in the June 2012 Journal of Human Evolution, after re-analyzing the carbon dating of a mammoth bone flute and a swan bone flute (one that plays a clearly-tuned series of C, D, F, and B notes), their creation now clocks in at somewhere around 42,000 to 43,000 years ago.

Image (1) brian-eno-2011.jpeg for post 313537

Why make this point?

1) Human beings have been predisposed to music for a very, very long time. You might even say it’s as if we are prewired for it. (That’s the good news.)

2) Although we face several important musical dilemmas at this time, it’s important to realize we view our dilemmas subjectively, as did our ancestors; to the cave dweller who carved that flute out of a swan bone, the possession of that instrument might have been pretty damned important at the time. (Or, then again, if they’d lost that flute while getting chased by a very hungry bear, maybe not.)

3) In a 2010 interview with The Guardian, Brian Eno stated that “the record age was a blip.” The age of these flutes demonstrates exactly just how small of a blip The Era of Recorded Music constitutes in music’s historical timeline: 0.026%.

That’s 26/1000th’s of all evidence-based music history; and music itself is thought to be much, much older. Many ethnomusicologists and archaeologists think it predates the use of language itself.

4) Therefore, “that’s the way it’s always been” is not how it’s always been. It’s just how things have been for 26/1000th’s of mankind’s total evidenced-based music history.

5) And just because something worked (somewhat) for the last 110 years out of 42,000 doesn’t mean shit. (You have to admit: that’s not much of a track record.)

6) Most importantly: Things Change. If they didn’t, we’d all still be playing C, D, F, and B notes on swan bone flutes. (And we’d still be getting chased by very hungry bears.)

Sometimes these changes occur rapidly – and sometimes, gradually. For example, the Era of Recorded Music didn’t start off with a bang but with a gradual shift away from the predominant form of popular music distribution of the time (sheet music), with recorded music not really taking off until the widespread introduction of radio – roughly 20 years after the initial introduction of the 10” disc.

Some music scholars have cued to that last analogy, equating our current societal shift to digital music technologies to the societal shift from sheet music to recorded music…

But the changes we’re experiencing today are not those of a simple change in format or distribution technology; in fact, we’d argue that the shift to digital technologies – across all creative fields – is similar to the technological and societal shift brought about in 1450 by Gutenberg’s invention of a printing press that used a moveable type system; in other words, something that may happen once or twice every thousand years or so.

Take a look at the parallels:

Prior to Gutenberg’s innovations, there were very few books; each book was either hand-copied or at best etched to wood block and block-printed, both limiting quantity and time-consuming to manufacture; control of information was in the hands of the few who could actually afford these books – the same people who also wielded power over an illiterate, information-poor populace, forcing this populace to accept their elite interpretations of knowledge…

And then things changed.

Gutenberg_Bible,_Lenox_Copy,_New_York_Public_Library,_2009._Pic_01

By 1500, there were over 20 million volumes, with printing presses numbering in the hundreds; by the mid 1500’s, that figure rose to 150 to 200 million volumes from thousands of presses.

With the advent of this revolutionary technology, information could be shared quickly and easily; and with this ability to distribute new ideas across borders and languages came the advent of mass communication. Education became available to many, as literacy erupted across Europe…

As such, this printing technology threatened the power that political/business and religious authorities held over the populace, as it allowed new, revolutionary ideas to spread like wildfire. There was lots of wailing and gnashing of teeth about the unwashed masses taking control (as well as book burnings, bloodshed, political and religious persecution); and yet, printed information just kept growing and spreading unabated.

Now, does any of this sound vaguely familiar to what we’re experiencing today?

Summing up The Renaissance and The Reformation in this manner is a gross over-simplification (be thankful, we could go on about this for days), but much like those who lived within those periods, it’s difficult for our society to understand the absolute enormity of the changes these new technologies have thrust upon us; it’s hard to see the forest for the trees… especially if the forest is huge and dark, the trees are massive and menacing, and there may be some hungry bears around and we can’t lull them to sleep with our swan bone flutes. (Yes, bears and flutes are a theme here.)

And massive change is never a pleasant experience to the people stuck within it; with all the wars and persecution surrounding them, we doubt many people living during the Renaissance said, “Man, this is great!” Yet changes of the magnitude we are experiencing today are not unprecedented: they just don’t happen that often.

So, in essence, we are living within an event that occurs once an eon – wrestling 21st century technologies exhibiting an exponentially accelerating rate of change as coupled to an equally exponentially enlarging volume of music and data…

… and trying to do that using barely-out-of-the-19th century methodologies.

Historically speaking, that’s a #fail.

When facing a change of this magnitude, the primary tenet of the 2,500-year-old I Ching – the Book of Changes – rings true:

We can act with change, or be a victim to it.

And since we doubt anyone involved in music really wants to be a victim, how do we “act” with this change?

Well… first off, we need to start thinking differently.

Rule No. 1: The Music Comes First.

Let’s explore three basic concepts starting off with a simple, yet often overlooked fact:

No two human beings ever hear the exact same song.

Once recorded, the song itself exists in a locked form: manipulations of sounds, silence, instrumentation, and timbre, held within time and space as an unchanging object. Viewed objectively, The Song Is The Song.

And yet, it’s not.

Think of it this way: at an arena show, even though we might be surrounded by thousands of other human beings hearing the same exact musical performance, we still view and listen to that music alone; this experience is individual to us, based upon our own unique set of life experiences up to the moment of that particular musical experience. This is why a performance may be a life-changing experience to one person, while, to another person, it was “just another show.”

The same principle applies to recorded music: each individual’s perception of that recorded object is different – not only based on their life experience up to the time the music is first heard, but also affected continually after it’s heard…

For example, think of a song you once loved that – unfortunately – you also relate to a relationship that ended in a not-too-great manner (it was a train-wreck, they ran your heart through a shredder, and so on). Your primary experience of the song has been tainted by the secondary experience you’ve associated with it; and depending on the depth of your personal disaster, it may take one hell of a long time before you can even listen to that song again – that is, disassociate the painful second experience from your initial experience of the song.

The song didn’t change: your perception of it changed.

The same principle applies to the passage of time: think of the songs you played over and over and over as a kid, and now when you hear them, you wonder, “What the hell was I thinking?”

Therefore, Basic Concept No. 1: A musical experience – and thus music discovery itself – is a subjective, not objective, experience. (One Size Does NOT Fit All.)

To demonstrate our next basic concept, let’s say you come across an album. It could appear on your stream, it could be something you rifled from a remainder bin, you could stumble across it on Bandcamp, or a vinyl copy appears at your door. You know nothing about this release; the artwork could be amazing, or it could be seriously meh-inducing, but what the hell, you decide to listen to it anyway…

And suddenly you have a HOLY FUCK experience – that moment when the music you’re hearing silences everything else in the world and the only thing you can concentrate on is how fucking good this music is and how it has already rewired each and every one of your neurons and possibly even your entire subatomic structure and then the sky rips open above you and a gigantic glowing hand descends from the heavens and gives you a BIG “thumbs-up” and you happily nod in absolute agreement and give it a BIG “thumbs-up” back and none of this exchange seems the slightest bit unusual to you since you aren’t thinking about anything at all except how this music is washing over you like a cleansing and renewing wave of cool, fresh water except it’s not water washing over you but a whole-body sweat generated from you dancing around the room ecstatically since you’ve lost control over your body to this music that now owns you and has already shaken you to the very core of your being and now you just can’t imagine living life without it and you just can’t listen to it loud enough and when it’s over all you want is more more MORE…

After you’ve recovered from this experience (or not), what’s the very first thing you instinctively want to do (other than listen to it again)?

You want to tell somebody about it.

You want to share that experience.

This Is How Music Works.

Basic Concept No. 2: Discovery is excitement; it is passion realized; it’s a HOLY FUCK experience to be shared.

And being able to tell somebody about it? That’s Word-of-Mouth.

Word-of-Mouth has always been music’s ace in the hole; it’s still the most powerful (and yet, due to Basic Concept No. 1, the most unpredictable) marketing technique known.

If a good friend with whom you share similar musical tastes were to show up at your front door dripping in sweat and clutching a phone, iPod, disc, or vinyl, spouting, “HOLY FUCK YOU HAVE GOT TO HEAR THIS SONG” – well, most likely, you’re going to be intrigued enough to give it a listen. (Or call the police, depending on the friend.)

And that sweaty friend who’s clutching their phone, iPod, disc, or vinyl spouting, “HOLY FUCK” at your front door illustrates the second part of a word-of-mouth experience: they want to receive a reciprocal outcome from sharing their experience, since they’re at your door hoping you’ll like it, too. (And also hoping you won’t call the police.)

In other words, Musical Word-of-Mouth is a two-part, shared process: a) being able to share a musical experience and, after having shared it, b) achieving a shared consensus about the music that can or will be shared again (Basic Concept No. 3, if you’re still counting).

So… when we talk about building a successful discovery or music recommendation methodology, we’re talking about building a system that can automate or facilitate a subjective word-of-mouth experience – one that actively creates excitement or engagement.

Strangely enough, it’s been built before.

The First Online Music Recommendation System

In the early 1990’s, Pattie Maes – an AI researcher at MIT’s Media Lab Software Agents Group, and one of the pioneers of software agent technology – felt frustrated when listening to Boston’s radio stations. A native of Brussels, Boston’s commercial radio scene seemed way too bland and restrictive – not nearly eclectic enough; worse, she couldn’t find any other way to discover new music to fit her tastes…

So, Maes and a group of research assistants at the Software Agents Group decided to prototype a new form of software agent named HOMR (Helpful Online Music Recommendation, later renamed “Ringo”) designed to function as a form of “electronic word-of-mouth.”

The idea was simple (but the work to build it wasn’t): create a software agent that would ask its user to scale-rate a list of artists, with users specifically advised to rate these artists for how much they liked to listen to them, thus creating a basic user profile. The software agent would then search its database, find like-minded users, compare this new user’s ratings to those of the like-minded user’s pool, and email back personalized recommendations based on that comparison.

This type of agent technology is now known as “collaborative filtering.” Limited forms of collaborative filtering existed at the time, but none had been put to use in such a novel and openly user-based manner. And it wasn’t “intelligent” software; it was an artificial intelligence built on a database of the total knowledge and choices made by its user base. As the user base grew and each user rated more albums, not only would the agent’s database grow in size – its accuracy in recommending new music would grow as well.

In 1994, Ringo went live as an email-based system – later introduced to the nascent World Wide Web using a bare-bones graphic user interface – and was enormously successful, especially for a quietly introduced academic project… but creating this agented instrumentality was only a part of Maes’ thinking: what really intrigued Maes was how a collaborative filtering agent could be used to foster and build a community.

In January 1996, Firefly – the first commercial music recommendation system – went live and blossomed immediately; but Firefly stood apart from its previous Ringo iteration in allowing its users to set up their own profile pages, write reviews, and most importantly, not only offering to connect the user to those other like-minded users to see what they were listening to, but allowing users to actually contact and communicate with each other, thus automating and fully facilitating the electronic word-of-mouth experience.

For those who were lucky enough to have used Firefly, it was an amazing experience. Talk to former users of Ringo or Firefly and they’ll tell you how much their record-buying skyrocketed as they were introduced to new bands they didn’t know existed and how it widened their musical horizons; and since the user base was made up of music geeks and active listeners (we’ll be using that term quite a bit shortly), by giving users a forum to expound on their enthusiasm, they then made new recommendations well beyond the ken of the recommender agent – all of which were fed back into the recommender engine itself. Firefly’s database grew rapidly and exponentially – as did the user base itself.

In its initial commercial iteration, Firefly worked like a charm. At its height, it boasted 3 million user profiles; again, this was pre-broadband, pre-download, and pre-Internet Bubble 1.0, so that number represented an outstanding installed user base when compared to the prime movers of that period (AOL with over 4.6 million users and Compuserve with approximately 2.6 million users)…

And, true to Maes’ thinking, it became a community – one made up entirely of new connections bonding over music they loved…

But nothing good lasts forever.

As the site boomed, the collaborative filtering technology behind Firefly caught the eyes and ears of advertisers and retailers of all sorts, and suddenly there was a mad rush to either license or develop competing collaborative filtering platforms. Firefly became a victim of its own success, diversifying the recommender engine toward other areas of interest, like appliance recommendations, causing Firefly to lose its primary identity – and many users as well.

In 1998, Firefly was sold to Microsoft during their Engulf and Devour phase for $40 million (pretty decent money pre-bubble). With the filtering technology much more meaningful to Microsoft than a hardcore gaggle of music geeks geeking out over music, Microsoft eventually shuttered the site in 1999, porting its technology into their new Passport software.

At that time, all that mattered to Microsoft was the agent technology used to aggregate and filter user data for purchasing recommendations – with the human interactive/community element that actually created that data completely removed from the equation.

And, in our opinion, that’s the exact point where automated music recommendation technology went horribly wrong.

Modern Recommendation – aka, The Mystery Grind

Recommendation and filtering technologies have changed radically over the last 20 years, and now music recommender systems are incredibly advanced iterations containing variants and hybrids of collaborative, content-based, knowledge-based or demographically-based filtering systems (amongst others) that use immense volumes of data points and multiple methodologies to replace a solitary participatory model.

pandora The Elephant In The Music RoomFor example, Pandora’s Music Genome Project is a famously “content-based” system of filtering based on a fairly small pool of music, with each song individually analyzed by a trained music analyst – a human being – against a list of 400 to 500 characteristics/data points that may or may not be inherent within the song, then combined into larger groups based on traits demonstrated in common; yet in practice, the Music Genome Project itself is only one of a core group of technologies in use by Pandora.

All of these current iterations are the result of tons of investment, extensive research, and some highly impressive work in a relatively nascent field. (According to an October 2013 RAINNews interview with Jim Lucchese, CEO of Echo Nest, only around 12 people per year graduate with advanced degrees in music information retrieval.) Beyond just the rudiments, these models are tightly held trade secrets, and the work behind them could well be considered “rocket science.”

Even though there have been incredible advances in recommendation technology, the decisions in how a music service decides to implement that technology (weighting in additional factors like existing popularity, product promotion, recommendation repetitions, and so on) ultimately becomes the weak link in the chain, affecting the overall quality of the recommendations…

And our society has become increasingly reliant on recommendations. We now lead exceedingly busy lives, and personal discovery can be time consuming; as we have grown accustomed to recommendation technology, faced with a growing mountain of information to sift through and diminishing available personal time, we have gravitated societally toward the convenience of being told what to look for or like; consciously or unconsciously, we want someone or something else to do our filtering for us, because we just don’t have the time to do it ourselves.

This could be why 75% of Netflix’s viewer activity comes from their recommendations; even if we feel that most of what is recommended to us is crap, we still continue to use this function because it beats the alternative of manually searching for things to watch (coupled with the frustrating experience of discovering that the item we’ve searched for doesn’t exist within their rights base).

The jury’s still out on the question of whether these types of recommender systems serve to fragment or homogenize choice; although there’s now a common acknowledgement that these non-participatory systems do drive consumer choice and sales, researchers know far less about how these systems actually affect markets and society as a whole.

What’s incredibly important, however, is to avoid dismissing the participatory community aspect of Firefly’s platform performance as obsolete, nor to confuse it with so-called “social music discovery.” The concept of “social community” is so much more than an app blankly spamming a line of Facebook newsfeed that says, “So and So is listening to The So So Glos…”

An active community enables a recommender to facilitate new associations without the use of a previously assumed association – that is, it allows a recommendation platform to make new, unexpected, yet completely valid and accurate associations based on individualized human interactions.

There are no music recommendation systems currently using this type of “explicit” data – that is, data derived directly from users engaging with other users – so today’s music-recommendation user has no real knowledge of what it’s like to receive active, community-derived recommendations. This experience is similar to a person who has never had coffee except for that liquid that oozes out of office coffee machines, made from mysterious no-name, pre-ground packets of an indeterminate age – or worse, “instant” coffee (a criminal blight on humanity we sincerely hope has faded into the distant past). To this individual, this coffee is the only kind of coffee they know; at best, it serves as a simple caffeine-delivery system and, as such, serves its function well (well, sort of).

This aspect also applies to recommender systems using “implicit” data: at least you get something that sort-of gets the job done.

When compared, however, to freshly brewed coffee made from freshly roasted whole beans, it’s damn hard to go back to drinking The Mystery Grind (except when you desperately need some form of caffeine, any caffeine, just to get through the day – in which case you’ll drink just about anything – or even chew the leftover grounds if necessary).

So, with very few users having “tasted” anything other than our current “Mystery Grind” forms of recommendations, their expectations are lower, and there’s no reason for them to get excited when it comes to music recommendations derived in this manner.

We are told that, in principle, within modern filtering technology, data can be implicit or explicit & derive the same results; but that explicit data is still much more powerful, as it cuts down on the “noise” factor (semi-related results that might not be accurate, like “if you like cream in your coffee, you’ll love coffee ice cream”) – and that discussion forums make far better communities than anything within current music services.

The power of this type of active community within a recommendation methodology cannot be understated: truly open, word-of-mouth interaction is having the ability to expound on and discuss something as arcane as why anyone on earth should listen to The Nutty Squirrels “Uh Oh, Part 1” (hey, it charted in 1958, and it’s a freakin’ ear-worm)…

the nutty squirrels The Elephant In The Music Room

And through this ability to actively channel recommendation, a community of like-minded individuals shares their passions and enthusiasm – that is, they get real freshly brewed coffee instead of The Mystery Grind – and they in turn get excited & engage, not just with the platform, but with other listeners. This effect creates an increased interest in the music and in the platform itself, thus generating a self-sustaining momentum from users who actively choose to stay engaged on the site longer and, more importantly, return often: the two most desirable factors for any site or platform’s success.

There’s Always a Problem

It would be an oversimplification to say that automated algorithmically derived discovery doesn’t induce excitement or engagement as effectively as models involving an active user base (although we firmly believe that); rather, all recommendation systems are affected by several factors that might be amplified from utilizing abstracted or implicit data, rather than explicit user data.

Let’s start with the problems of “cold-starting,” “weighting,” and “recommender persistence.”

Cold-starting is just that: the first time you use a service, the service needs to build your user profile from scratch; this step really hasn’t changed much since Firefly…

Yet, our newer services operate from the perspective that over the past 20 years, users have come to expect any app or platform to work immediately and therefore believe they need to grab you from the get-go – engage you or lose you – so they must throw something at you to get you going.

This type of cold-starting problem currently gets addressed by the user selecting music from a list of genres, or a service extracting a playlist from a “seed” song that the user suggests, or from asking the user for a very short list of favorite bands or musicians; and then, as the user listens to presented choices and up- or down-votes the song or album (either explicitly or by skipping the choice entirely), a more detailed user profile gets built. It’s quicker, but it doesn’t generate truly accurate results unless the recommendation service gets used heavily.

Other services start recommendation immediately using demographically or geographically derived implicit data; for example, if Spotify’s recommender could access our current iTunes database that it pulled into its desktop player (well, 68 days of it – we needed drive space, so we trimmed things down a bit), it would have gathered some actual music data as a starting point. But when we open the app, what pops up on the main discovery screen?

Gordon Lightfoot. Three Dog Night. Harper Valley P.T.A. by Jeannie C. Riley. Darius Rucker? Michael ‘Effin Bublé? TONY ORLANDO AND DAWN?

This is The Music of Our Nightmares. (Sorry. Just had to bitch about this.)

What Spotify is doing is weighting their initial recommendations demographically to relative age and the geographic area we live in. (Yes, we live in Hell.) Each recommendation is followed by a qualifier: popular in your area, popular when you were in school, huge when you were a teenager, and so on.

As you search out music to listen to, Spotify learns from this data and contours its recommendations accordingly; still, even if it had peeked at only the first song in its own player’s directory (12XU by Wire), perhaps it wouldn’t have thrown Tony Orlando And Dawn at us. (And we have used Spotify, so the agent should have known better.)

As is, allowing a service to search an existing base of songs on your device – while getting much closer to actual explicit data – still introduces a great deal of noise into recommendation data, since most people don’t actively rate their collections; but searching pre-existing libraries also runs into the wall of user privacy – the user must actively grant access to this process – and therefore could be legally problematic. Services like Rdio actually do give the user the ability to allow Rdio to import their existing iTunes database and then adds more music from artists already existing within the user’s “collection;” but at least from our experience, this data does not filter into their recommendation base, and the service instead bases its recommendations on what you have played through the service itself.

A different type of cold-starting problem also comes from the introduction of new bands or musicians: no one knows this material, so how does it get recommended?

Through analyzing the music, data points pertaining to this new music are generated; this data is then entered into the recommender database and made available… but wait: if that’s the case, why don’t you see these albums recommended to you immediately instead of music by more well-known musicians?

This is the function of “weighting.” Weighting is primarily found in the process of further recommendations: as the listener uses the service, the recommendation system gathers usage data and relates it to its database; at that point, however, certain components are combined in an effort to represent the best possible results the recommender can associate to the listener’s current choices. In other words, the recommender’s best guesses are weighted by data points such as popularity, name recognition, reviews, press, PR, or any of thousands of search-gathered factors – with name recognition and popularity often the top two factors.

Yet those weighted results may not be what the listener is looking for, thus producing a “false positive”; and depending on the service’s interests, this step can also become a point where false positives can be purposely introduced. (We’ll get to that subject very shortly.)

For weighting to work well, transparency of this process becomes vital; for example, Rdio’s weighting mechanism within their “station” function is by far the most transparent, in that it allows the user to progressively remove weighting from within their station recommendations by allowing the user to “dial up” the degree of “adventure” to the recommendations – leading to artists or tracks that have not received as many plays or are wider in relation to their initial starting point. (Kudos for that feature.)

Still, some recommender systems have no transparency at all to their weighting methodology, so you really don’t know much about why something has been recommended to you – possibly lowering trust in the recommendations and thus lowering engagement. (We’ll cover Trust shortly as well.)

Finally, repetition of a recommendation gets featured within some services; its use comes from the advertising concept of “effective frequency”: the belief that one impression never completely registers with a viewer or listener – and that it may take between 8 to 40 repetitions to register a true impression. Repetition in music services, however, can be viewed by a skeptical user as an attempt to push an artist onto a listener – who then questions, “Why the hell does (name of service) want me to listen to Chubby Checker?” – and that, too, can lower the user’s trust in the recommender system.

Trust Us: We Know What’s Best for You

Automated recommendations really have no value unless the user consciously or unconsciously trusts the recommender system. We often use “trusted sources” surrogates for this function in daily life. For example, even with our ability to click and listen for ourselves, a review from a respected writer who tells us to stick with a whole album – or that a new release will grow on you – can often tip the balance of whether you give a track or an album its full due instead of skipping ahead after 20 seconds.

This was an area where active human recommendation systems excelled, due to word-of-mouth; fully automated systems, however, do not give that depth of insight currently – usually all you’ll receive is a “Because You Listened To…” line as a reason for the recommendation.

Trusted sourcing falls one step below word-of-mouth in recommendation effectiveness; these recommendations are not personalized but can be used as a substitute methodology for word-of-mouth in two ways: 1) you trust the source’s tastes, and therefore you’ll give it a go or 2) you can’t stand the source’s tastes, so you end up using the “if they liked it, I’ll hate it” protocol – or conversely, the “if they hated it, I’ll love it” protocol. (The latter works extremely well with film criticism.)

playlist The Elephant In The Music RoomA newer substitution for trusted sourcing is the use of “curation.” Playlisting can be a form of curation and, depending on the source, can serve recommendation well; yet it still does not carry the weight of true word-of-mouth recommendation unless the playlist is created specifically for an individual as if it were a personalized mixtape. (The nice thing about mixtapes: since everyone knew how long it took to plan and record a mixtape, they were always personal – and possibly the penultimate form of musical word-of-mouth.)

Still, “curation” moves one step further away from trusted sourcing itself, as it delegates sourcing to the generation of thousands of playlists built by known personalities, critics, genre classifications, and unknown sources…

… but if you think about it, this type of curation could possibly negate trust itself; for example, in viewing a highlighted celebrity’s curated playlist, how do we know that this person actually produced this list? Could it have been created by an assistant? Were they paid an endorsement fee to create this list? Are the tracks truly representative of what this celebrity listens to, or are they just paid placements as well? And do these lists serve to dig deeper into music’s immense catalog? If we trust curation blindly, we’re fine; if we don’t, well…

And that pretty much sums up the main problem with curation: it brings you right back to some of the same issues introduced in weighting (you have no idea why this track you have never heard of is there, how it was chosen, if there was an agenda behind its placement, and so on); but unlike weighting, with curation you are actively choosing to abdicate your choice to someone else you do not know, nor know their agenda (as we have done for the better part of the last century with radio and television). You are expected to simply accept what you are given and move on.

For this reason, curation does not serve as true recommendation; it’s simply a different approach to a “lean-back” style of casual listening as provided by services like Pandora or terrestrial radio…

And due to new factors involved within rights-base licensing, there is a distinct possibility (some would say probability) of curation eventually falling away from any true recommendation purposes to become a form of paid promotional streaming – and not true discovery.

Fractured

 The Elephant In The Music RoomUnlike the Firefly model – a model that had no restrictions to the music it could recommend – today’s recommendation engines are restricted to making recommendations within a finite amount of music that a particular store or service has licensed for streaming or for sale. A common misconception is that the “magic number” of 20 million songs means that all major music service rights bases are identical; but as discovered by Liam Boogar, editor of the French start-up and tech blog Rude Baguette, in doing a test of importing playlists from Spotify to Deezer, Boogar found there were songs in Spotify – the smaller rights base – that were not within Deezer’s much larger rights base.

This is an example of “fracturing” (also known as “fragmenting”). Not all services carry the same music for reasons of library size (as noted earlier, Pandora’s base is barely over 1 million songs), exclusivity (Led Zeppelin’s streaming catalog, assigned only to Spotify), rights licensed on a country-by-country basis, or limitation by some digital aggregators that exclude certain stores and services from their catalogs (or vise versa).

In essence, there is no single universal music rights base or database: the music base is therefore “fractured” – not uniform – especially when considering outlets like Bandcamp and free music service outliers whose music base never gets included in the “magic number” due to their operational models.

Yet there are two newer forms of fracturing on the horizon that are much more ominous in nature.

In November 2013, Deezer announced on its blog “a pioneering collaboration with acclaimed Icelandic newcomers, Mono Town,” giving Deezer an unstated period of exclusivity to Mono Town’s music. This is noteworthy for two reasons: 1) Mono Town is a new band, not one with a catalog and 2) this deal went far beyond exclusivity: the service actually possessed a proprietary interest in promoting an artist – as if the service were instead acting as a label – thus opening the possibility of skewing its recommendation results in favor of proprietarily monetized streams; that is, if Mono Town were to appear in your Deezer recommendations, would it be due to your love of Icelandic Pop or that Deezer has a proprietary interest in you listening to them?

Moreover, Deezer states that this agreement “is just the start of what we hope to be a music industry revolution” – that is, more agreements of this type, not only fracturing the rights base further but crossing some ethical lines in the sand.

Speaking of crossing ethical lines, the other much more ominous fracturing comes by way of Clear Channel Communications, the largest radio station group in the U.S. and creators of iHeartRadio; in facing a distinct near-future possibility of legislation that would require U.S. terrestrial radio stations to pay performance rights, Clear Channel has been quietly doing an end-around of the CRB/SoundExchange compulsory royalties process and making favorable direct-deals with small labels like Taylor Swift’s Big Machine and Glassnote…

… but the real coup occurred in September 2013 when Clear Channel made a deal with Warner Music Group to collect royalties for on-air play of WMG product, in exchange for receiving more favorable rates for their iHeartRadio streams – and better yet, to sweeten/enable the pact, WMG artists will also get extensive promotion both on-air and online.

Now, before you start screaming, “That’s payola!” In one of the niftiest sleight-of-hand movements in radio’s spotted history, as reported by Ben Sisario of The New York Times, Clear Channel has stated that its on-air promotions and play of WMG’s artists would not violate payola statutes because this promotion & play would be considered a part of its commercial inventory and not part of its regular programming. (Pretty slick, eh?)

And as for the streaming side of the equation? Music distributed through digital transmission streams is not subject to over-the-air payola statutes, so this is a clear win-win for Clear Channel.

In his comments to the New York Times, David Macias of Thirty Tigers states the WMG deal “could have a chilling effect on independent artists and what creative expression is going to look like.” But in his scathing Hypebot op-ed, Macias goes even further, writing that when radio and digital are “financially incentivized to play music only from certain sources, and you’re on the outside of that, you are less likely to have a career, whether you are an artist or working in the service of one.” Finally, he also states, “The market for pre-recorded music just got less competitive. And I respectfully suggest that you ain’t seen nothin’ yet.” (We wholeheartedly agree.)

Buses, Whales, Listeners & Logic

Okay, now we’ve come to the part of the movie where we start to see the wheels slowly falling off the speeding music discovery bus…

… y’know, the bus that’s carrying all the current economic models for the entire recorded music business. That bus.

Fred Wilson, a leading venture capitalist with Union Square Ventures (whose portfolio contains far-sighted leading investments in Kickstarter, Soundcloud, and Twitter), characterizes subscription music services as “proprietary commodity services” and points out that it’s nearly impossible to share recommendations across services due to the very nature of their business models: a missing functionality that hampers music discovery, listening, and growth…

And growth is the No. 1 factor here….

Right now, on the surface, things may appear to be growing – “wow, look at all those quickly multiplying number of streams!” – but with the very nature of these siloed business models artificially imposing limits on discovery and recommendation, it’s more likely we’re starting to stagnate instead…

Because even with the services that exemplify the very best and most progressive discovery systems available today – Bandcamp with its “fan pages” and music feed and Rdio with its ability to dial up the listener’s level of discovery – the walled-in nature of all siloed services has severely fractured the true base for listener recommendation and usability, limiting music discovery and market growth under the guise of “competitive” or “proprietary” models.

Now, as these services grow, the cost of their licensing rights grows even faster…

Ooooops. We’re getting ahead of ourselves, and we want to save the speeding, out-of-control bus swerving and then rolling over and over on its side only to crash into all those giant, flammable music storage tanks and the ensuing multiple explosions photographed from eight different angles in slow motion and THX®surround sound and all the carnage and screaming and all the burning people and mayhem – and all that other good stuff – for the end of this movie.

(By the way, that’s called a “foreshadowing tease.” Get your popcorn ready.)

Instead, let’s do a quick cutaway (with yet another piece of foreshadowing hidden inside it) and look at music’s actual audience…

On Paul Lamere’s Music Machinery blog, there’s an excellent post about his Zero Button Music Player project. Within that post, he quotes and actually graphs out David Jenning’s summary of Emap’s Project Phoenix, a pair of UK-based music studies, into behavior patterns among music listeners.

Those studies identified four basic levels of musical interest among its participants: “savants” (everything in life is tied to music), “enthusiasts” (music is key, but there are other interests, too), “casuals” (music is welcome, but other things are more important), and “indifferents” (who “would not lose much sleep if music ceased to exist”; it’s hard to imagine people like that exist, but, evidently, they make up the majority.)

We’ll group these categories one step further into two basic types – “active” or “casual” – using as our dividing line whether music was important or not to the respondent’s life; in doing so, we see that “casual” music listeners are by far the larger group (72%), while “active” listeners make up 28% of respondents in these studies.

You probably know what a casual listener is, but what constitutes an “active listener?”

Simply put: the active listener seeks out music.

In the past, they were the ones who rifled through used and remaindered sections in record and discount stores, always looking for the score everybody else had missed (sweet!); they dug through import bins and searched out obscure band catalogs; they were the people who knew Nick Drake’s entire catalog by heart long before Audi used Pink Moon in a commercial soundtrack, which finally introduced his music to a wider audience over 25 years after his death.

Active listeners are influencers: it’s their passion and enthusiasm that carries over to other less active listeners, and that momentum continues toward borderline casual listeners, who then spread the word to others; they embody the starting point for artist exposure.

Lorde-The-Love-Club-albumFor example, Lorde’s “The Love Club” EP first appeared as a free Soundcloud download in 2012, backed by very little non-traditional PR; for an unknown New Zealand artist with no track record to become recognized, somebody had to be the first listener – an active listener – who then passed their enthusiasm on to other active listeners and so on… and you know the rest of the story.

Uh oh… we used the “free” word, which means someone is bound to bring up a “dark” point about active listeners, so we’ll bring it up ourselves: yes, active listeners have also been the largest group of active file sharers.

But let’s be straight about this: active listeners are not necessarily file sharers. File-sharing behavior simply illustrates the musical appetite of active listeners; and yet, for all the noises using the recording industry’s favorite über-scary pejorative term – piracy – study after study after study has revealed that file sharers are also the most active buyers of music.

And they buy music, not just rent or lease it (as in streaming). They collect it.

Back on-point: with digitally licensed services enabling file sharers and active listeners to discover new music through legal means, it’s now essential for the recorded music world to re-embrace active listeners and develop discovery mechanisms that allow them to easily work their way through our current, massive maze of music.

The reason is simple: to use a software/hardware analogy, active listeners are music’s equivalent of “early adopters”; they’re the experimenters, the ones who will try something out when no one else will. They do all the heavy lifting.

The act of creating music is an organic process that builds upon all music that has come before it. You can even visualize the world of music creation as one self-contained ecosystem, with mainstream music and casual listening at the top of the food chain, with this ecosystem constantly fed by experimentation and change that almost always comes from unknowns, outsiders, outliers, and scenes far distant from the mainstream.

Better yet, think of the mainstream music world (and legacy artists) as blue whales: the largest mammal on the planet.

Blue whales feed almost exclusively on krill, some of the smallest creatures in the ocean; krill feed on phytoplankton, microscopic, photosynthesizing organisms that actually sustain the aquatic food base – with phytoplankton reliant on micronutrients welling up from the ocean’s deeper waters to grow and multiply.

If the ocean’s health becomes incapable of sustaining an adequate supply of micronutrients, you have no phytoplankton.

If you have no phytoplankton, you have no krill.

No krill? No blue whales.

Without care for the smallest elements within an ecosystem, stagnation or extinction occurs.

Now, we’re not making this example to endorse a movement for all mainstream musicians to Eat an Unknown Musician Today (though that would make for some radical bumper stickers). It’s just a method to illustrate how music that may seem a bit odd or off-the-wall eventually finds its way into or feeds new mainstream music movements.

And this is why discovery is vitally important to all musicians, including those at the top of the charts: in essence, the active listener is the initial nutrient base for music that’s new or different. If you do not create a hospitable environment for this nutrient base that feeds and nurtures this element of music, eventually the entire ecosystem will stagnate or collapse.

To put it bluntly: the active listener fosters music discovery and music growth. Care for them – or face the consequences.

Now, look back at those Emap Project Phoenix graphs again: 72% of listener types do not make music a priority – that is, they do NOT spend money on music…

And yet most of our current music services view the larger casual listener percentage as the true untapped potential mass audience and are directing their efforts to capitalize on that potential audience. (More about this in our upcoming Exploding Bus section.)

So, wait… doesn’t that model seem a little “out of whack?”

Why not concentrate on the 28% of listener types who DO make music a priority instead? Why not pour our efforts into revitalizing that sector?

Why not try to grow the base instead?

If we truly want our music ecosystem to thrive, then perhaps it’s time to think about building a new all-inclusive, non-weighted music recommendation platform – and at its core, an open community of active listeners generating explicit recommendation data.

Yeah, we just said that: build a new active recommendation platform.

By developing a favorable discovery environment that allows active listeners to gather and explore this immense volume of music, not only will this recommendation environment become self-sustaining, but, more importantly, we can design an environment that will actually grow the base of active listeners rather than simply try to divide the existing resources (the mainstream pie of casual listeners) into smaller and smaller pieces.

And with Spotify taking The Echo Nest out of circulation and eventually hoarding all their recommendation resources for itself, simple logic leads us to believe that now would be the ideal time for somebody to jump into this void and build a new cross-service recommendation platform, with features specifically designed to enhance continued engagement and growth.

What kind of “specifically designed” features?

We’re glad you asked.

Growing the Base

Before we start throwing ideas at you, allow us to state two things for the record:

Designing an all-new discovery platform is not a trivial task. A truly elegant platform would take a truly elegant hack team, combined with a truly elegant UX (user experience) team in addition to a truly innovative core of music data specialists. This task isn’t something you’d do on weekends in between mowing the lawn, grocery shopping, and throwing a few burgers on the grill. We’re not in any way saying any of this would be easy, under the best of circumstances: it’s serious work.

And we are in no way experts: we’re just a couple of geeky musicians with backgrounds in the technology and entertainment business sectors who can barely code their way out of a paper bag, but who also possess an on-going, almost morbid interest in the technological side of music. So, we’re just spitballing some ideas here to make you think… and hell, after looking at these ideas, maybe you will come up with even better ideas.

The Basics:

• Build it as an interactive platform, not an app or service, but a fully-fledged platform with an open API (application programming interface).

Using the open API and other services’ APIs, recommendations could be directed to the user’s service of choice (set up in user preferences), or if the recommendation exists outside their standard services (say if it was on Bandcamp or a Creative Commons or free music service), the platform could automatically redirect the user to that particular page. With a solid API and an effort to forge bonds with the various services, it should very well be possible to allow redirects from those services right back to this recommendation platform. (Actually, it would be advantageous for those services to do so, since they would also benefit from increased user engagement. But hey, corporations and people can be stubborn…)

The reason for directing outward to existing services is not one of choice but one of necessity. Unfortunately, if we want to create an all-inclusive database for recommendation, we must accept the horribly fractured state of our current music base.

Sales and streaming services rely on label aggregation or independent musicians who have submitted their music through contracted third-party digital aggregation services. But there are a growing number of musicians who have stepped outside those bounds to artist-friendly direct-to-listener platforms like Bandcamp that offer a better level of return than any other service, as well as more control over their music. Still, others gravitate to tip-based services like Noisetrade or use free-distribution “social” platforms like Soundcloud. And then there are outlier services like JamendoalonetoneThe Internet Archive, or The Free Music Archive (among many others) that have been created with the intent of offering Creative Commons or freely based music distribution to musicians who wish to adopt that model for philosophical reasons.

Since our goal is to come up with an all-inclusive recommendation platform and community, with this much fracturing (not to mention the almost insurmountable maze of music rights negotiations), it would be nearly impossible to host music on the platform itself. So, creating a “full-duplex” redirect capability would probably be the next best method.

(Hey, the music world is a mess. You rebuild it with what you have on hand.)

• The design of the active community element must be built within the platform’s foundation. It cannot be “tacked on,” as it was in Ping (Y’know, Ping? Apple’s failed “social” music experiment inside iTunes? Don’t remember it? Good.). Nor can it be created simply to push music sales on an existing service (hello again, Ping). That’s instant death.

Build an actual community platform; don’t repeat the Ping mistake.

• Everybody loves eye candy… but if it gets in the way of function, it becomes a deterrent to users. There are several services – you know the ones we’re talking about – where site/service navigation is sacrificed for appearances. This leads to listener frustration, and that gets in the way of engagement. Anything that gets in the way of listener engagement must be sacrificed. It’s possible to find a balance between function and design, so find that balance.

• We know people will think we’re absolutely insane for even suggesting this, but forget about building integration into standing social platforms like Facebook or Twitter. To hell with them. Facebook and Twitter both viewed music as an afterthought, a method to gather more mineable or salable user information (and they still think that way), and both failed at music miserably. Again, why repeat the same mistakes?

As crazy as it may sound, the idea here is to forge new connections and let the user experience drive the platform. If this platform is any good at all, it will draw more people and grow organically.

• Now here’s a major policy point: privacy should be assured – that is, no individual user data will be brokered outward. (What? Not sell user data? Hell Yes. Make that a point, and watch your initial user base skyrocket.) User profiles would not be sold, nor data sold for advertising; profiles would only be used to compile recommender engine data, with group and individual discussion capabilities built in to foster networked and automated word-of-mouth music recommendation.

Anonymity could be preserved as well (unlike Google models), allowing a fear-free flow of ideas and fostering a truly humanized word-of-mouth recommendation system for both its users and the recommender engine itself.

The point is to make this community a safe place: one where music – just music – gets discussed and recommended, and everything else stays outside. We have enough places where bullshit raises its ugly head. Wouldn’t it be nice if there was someplace where it didn’t?

The main idea here is to stop following the crowd – and instead, Lead.

Have a Clear Platform Philosophy:

• You are trying to build a new community of like minds around one central belief:

Music is important to me, and I want to hear more. Show me where to find it.

Put the individual listener first. Your listener is not a wallet or a “consumer” or a “fan” or an “asset”; your listener is an individual – and therefore deserving to be treated as such. (Musicians, take special note of that statement.)

In doing so, you dedicate this platform to being a truly “honest broker” as a recommendation system; that is, nobody can pay the system money to recommend false positives to the listener.

In fact, this should be the primary promise from the platform to the listener: We’re not trying to sell you on something we think you might like; we want to help you find something you will like. This statement by itself would immediately engender a bond of trust.

The Introduction Process:

• Contrary to the idea that a platform needs to be instantly usable to gain a base, actively promote the steps and time needed to build a solid user profile as a one-time process toward better, more accurate, music recommendation. Let others be the early speed on the racetrack and cede casual listenership to lean-back platforms; instead, be a Closer – a platform that rewards the active listener for their time. In fact, if done correctly using a strong, easily navigable user experience, you might be surprised by your conversion rate as word gets out about this platform.

• You could even “gamify” the profile-building process and the recommendation process as well. After all, you want the user to be engaged. You want the experience to be fun, so gamification would therefore fit well into this scenario.

• The listener would log in, enter their basic user information and preferences, then create a list of current favorite artists, regardless of genre; genre should have no part in this initial building process, as it might get in the way of actual meaningful data collection. Encourage the listener to enter as many artists as possible – again, gamification would work well at this stage, urging the listener to jump in, feet first. Make this experience enjoyable.

Once that stage is complete, several options could be used to fine tune the recommendation experience. The recommender could present the listener with an associated list of related artists or albums that could be scale-rated on how much the listener likes listening to them (the Ringo/Firefly method); or it might also be possible to license a small pool of music to serve as “test-bed” tracks, based on their initial choices. The listener could then be asked specific questions about these tracks – not as simple as “did you like/hate that?”, but instead scale-rated multiple-choice questions about what it is they liked within the song (beat, instrumentation, vocals, lyrics, and so on) and correlate that refined data with the other data existing within the user profile.

In the next stage, the user would be introduced to the community element of the platform; Recommendations could be presented directly to the listener, as well as music choices from listed like-minded listeners. And at that point, the user could decide if they wish to remain anonymous, if they wish to operate pseudonymously, or if they wish to possibly interact with these individuals, depending on the preferences provided by these other individual users.

The new user could then be directed to several possible discussion forums as well. Engaging in these forums would make further associations outside the ken of the recommender, with that information feeding back to the engine – if permission is granted by the user. Always defer to the user.

Create Divergent Discovery Pathways:

• Not only would this discovery platform allow for listeners to engage with each other, but methods should be brought to fore to allow musicians to engage their listeners. In our somewhat disassociated society, interaction with listeners is truly key to building a musician’s support base. But let’s not stop there…

• Music writing truly fills a need for many active listeners and provides yet another path toward discovery. An almost magazine-like environment could function well within this platform, and it would be awesome to pay writers to really do their thing and write about the music that excites them.

In fact, writers could be incentivized to write about music that’s “under the radar” without pitching their hearts out. Let writers write about the music they love – music they’ve been unable to write about before or music that isn’t getting any coverage. This adds to the platform’s cachet as a true music discovery platform and increases the trust bond with listeners.

(This could also be done as an adjunct to existing publications or blogs. And as with music service interactions, it would be to their advantage to join in… because hey, their biscuits are in the fire here as well.)

• Further gamification could be used to introduce new titles into the recommender stream. For example, think of offering some sort of bonus to someone who was among the first 10 listeners of an album. This type of approach could jump-start the cold-start recommendation process for these new unknown releases or musicians.

The idea here is to incentivize the discovery process. If there is so much music to be processed, well, why not distribute the processing? People can do the job much better by generating explicit data, so the informational signal quality would remain high.

• Playlist building and sharing could also be just as big of a function, and with writers, listeners, and (hopefully) musicians all being involved, everyone can share their music choices openly, not just “connoisseurs” or “aficionados.”

• In essence, allowing multiple discovery models to co-exist within the same platform would allow the listener to choose or vary the manner in which they choose to find music; on some days, you might want to dig into discovery, and other days you might just want to read or generate a list and so on. Again, one size does not fit all. A platform that recognizes a listener’s varied methods in how they approach music at any given time – allowing them to navigate through music discovery in whatever manner they choose, without locking them into a single model or methodology alone – respects the listener and grows the bond between platform and user.

The True Secret Recipe of Awesome-Sauce:

• This secret is dead simple: make discovery fun again. Entice the listener with substance and not just eye candy; give them easily navigable and customizable functions rather than limitations. Create an open methodology that encourages engagement; create a place for them to hang and discover without pressure or hype, and create a destination that a listener wants to jump into again and again.

Do that, and you will have created the Killer Platform.

________

The point of this exercise: all of these capabilities exist now or are currently in development.

For example, The Echo Nest’s Rosetta Stone initiative now allows for cross-service interoperability, with an API already available (in fact, they offer it free for non-commercial uses, but with the Spotify purchase, whether that will continue into the future is anybody’s guess); this type of functionality could be spread easily across the vast spectrum of services (subscription or otherwise) to be all-inclusive, since the search crawling necessary to gather data points is an ongoing process.

And by making this a music-only platform – no toaster recommendations this time – it creates a protected ecosystem to foster true music discovery. In fact, borderline casual listeners could be drawn to it by its unique community nature and integral functions to drive playlisting or lean-back programming; and yet, active listeners could dive as deep into discovery as they desired.

Most importantly, by building enthusiasm and active participation with a fun discovery platform that even allows casual listeners to join in and find new things they never knew existed,  you grow the base of actual listeners, revitalize interest in music, and enlarge the prospective music audience, who can then both support musicians and strengthen the entire musical ecosystem.

Give people a reason to be excited about music again, and everybody wins.

And by now, you should have thought up some really great ideas, too. So, how do we make this happen?

Uh Oh. Here Comes the Exploding Bus…

Unfortunately, especially now that we’ve built up your hopes with one possible methodology for music’s salvation, it’s time to watch the music discovery bus swerve, crash, explode, and start Blowing Shit Up.

There are plenty of roadblocks and obstacles in building a model of the type we’ve outlined, but right now, the biggest obstacle is money…

And we’re not even talking about how to monetize this model once it’s in operation. Hell, we’re just talking about gathering the initial investment to build it.

Starting up a new music service (or music platform) today is pretty much of a non-starter; while there are still some new services in the wings for 2014, and while some of the larger services have been able to raise more investment capital in recent months, getting venture capital to invest in a new standalone, music-related service is nearly impossible.

The primary reason is simple: with the exception of Pandora (which winked at being profitable in its February 5th quarterly report, and then immediately issued guidance that they wouldn’t be profitable in the next quarter, saying, “It’s not a time to try and optimize profitability“), none of the current standalone streaming services – none of them – has ever made a profit; in fact, a shake-out of these services may have already begun: Rhapsody and Rdio have already opted for layoffs (Rdio’s reason for its layoffs: “to improve its cost structure”), and the darling of 2011, Turntable.fm, shuttered its rooms in November.

A secondary reason why capital wants nothing to do with new music services comes from the costs involved in music licensing: there’s a problem with your economic model if you’re paying out 60% to 70% of your gross revenue for a single resource; the Copyright Royalty Board’s compulsory licensing rates negotiation process for 2016-2020 started in January, and with most analysts expecting non-interactive rights rates to explode upward, this economic model will become even more unprofitable for interactive and non-interactive services alike.

A third reason for a reluctance to invest in new services comes from established, diversified corporations entering the marketplace; with Apple and Google now fully engaged in streaming and Amazon still looking at jumping into the pool, standalone services – whether for sales or streaming – have become even more unattractive investments. After all, they have no other business to fall back on, while diversified corporations can easily accommodate necessary start-up losses as their services are designed as “potential” profit centers added as “features” to their respective hardware platforms. (This is how iTunes started: designed originally as a break-even endeavor to drive platform adoption.)

But the real hammer slammed down onto the anvil with the Jan 14th decision by the U.S. Court of Appeals for the D.C. Circuit that struck down the FCC’s Net Neutrality rules. Fred Wilson eloquently demonstrated what you might expect in a year from the VC world in his blog a day later. (Recommended reading if you don’t understand what this ruling means to services and users and why you should be screaming at the FCC to regulate broadband as a common carrier under Title II of the 1934 Communications Act, which is what they should have done in the first place.)

Very basically, the U.S. broadband market lacks any real competition, and without some form of net-neutrality regulation, ISPs can charge a deep-pocketed service a premium for providing guaranteed bandwidth (AT&T has a quaint term for it: “sponsored data”) while still whacking individual users with broadband caps. Or, better yet, favoring a service by saying that if you use that particular service, it will not count against your broadband cap, while other services with not-as-deep pockets or agreements will run directly into that wall. In other words, broadband providers can now pick winners – and losers.

And broadband caps and overages will become an issue for individuals: as data streams fill with higher-quality visual content and moving image technology (like 4k HVEC video), your data plans and broadband caps will start taking a serious hit. Even several deep-pocketed corporations see the writing on the wall. Look at Google Free Zone and Facebook Zero, options being rolled out overseas that strip out photos and provide basic data to users without the user incurring data charges – all provided, of course, through negotiated agreements with carriers.

Add in the fact that ISPs – mostly cable companies – have a vested interest in keeping you engaged with their current service models. So, without net-neutrality agreements in place, it will not be considered anti-competitive to promote their own services (even over those who might ante up for better bandwidth) or block other platforms from displaying streamed content (just ask Playstation 3 or Roku users who want to watch HBO Go using their Comcast connection). And heck, with cord-cutting becoming a serious factor in cable subscription erosion, a lot of ISPs would like streaming of any type, video or audio, to go away entirely. (For more on this aspect, just keep watching the battles between Netflix and the studios, channels, and cable companies to lock down long-term programming rights.)

So, with the U.S. Court of Appeals throwing out the FCC’s current rules on net neutrality – and with the FCC deciding not to appeal that ruling, but instead opting to work within Section 706’s untested (and frankly, weak) set of rules on a “case-by-case” basis – the crowd of music services we have now, plus the ones who have significant investment in hand and are waiting in the wings to launch, will probably constitute the entire cage-matched field of entrants heading into a new corporatized remake of Highlander.

And make no mistake – a lot of heads will be getting lopped off: Spotify’s pre-emptive purchase of The Echo Nest marks the first real swinging of the swords – a move that will eventually separate most of their competitors from their current primary data source (both Rdio & Rhapsody have already announced that they’re severing ties to The Echo Nest); and with the entry of prime-movers Deezer & YouTube still on schedule for this year, this current marketplace isn’t just top heavy – it’s about to turn into a bloodbath.

Always Crashing in the Same Car

But wait, there’s more! Much more.

At the beginning of this article, we stated that lack of discovery posed an “incredibly complex set of problems” – we’ve pretty much covered that – but then we also stated that lack of discovery presented “potentially catastrophic ramifications for most musicians and one that threatens the livelihood of the recorded music industry itself.”

So, really, how bad can it get?

In the appeals process to the March 2007 decision by the Copyright Royalties Board that set new statutory webcasting rates and removed the “cap” on the per-station/channel fee – a decision that effectively put thousands of small webcasters out of business and all but killed grass-roots Internet radio – several analysts inferred that the CRB’s decision could possibly set up an economically unviable system. As rates climbed, the thinking went, it would be nearly impossible to keep up with the cost – the bigger you’d get, the harder it would be to post a profit; and usage growth could never catch up with expenses.

Well… a just-released February 2014 report by UK-based industry analysts Generator Research now states unequivocally that “the music subscription sector is intrinsically unsustainable” – not only backing it up with financial analysis but also joining a rising chorus of analysts in saying Pandora’s public stock is vastly overvalued (The New Yorker’s John Cassidy has calculated that Pandora is the 2nd most overvalued Internet stock on the market) and recommending “shorting” Pandora – that is, betting against it and all other subscription streaming services.

With most musicians still not prepared for a streaming world – much less a post-streaming world – this now sets up a horrific set of circumstances…

So, allow us to present you with just one recipe for a worst-case – yet entirely possible – scenario:

By concentrating on mainstream casual listenership, many services are making a huge bet that they can achieve audience subscription penetration levels similar to that of a video service like Netflix or SiriusXM’s satellite subscription radio model. But by assuming music is merely more “content” – based solely on the concept of product consumption levels – and failing to compare music listening behavior to video and film viewing behavior is a stunning miscalculation, blindingly absent of any long-term historical understanding of music or human behavior. Watching on-demand television and listening to music are two entirely different behavioral habits – it’s like comparing Apples to Jackhammers; and yet you see this poor behavioral assumption and comparison tossed around all the time in press releases and interviews (scroll down, 8th paragraph from the bottom)…

sirius xm The Elephant In The Music Room

And if comparing streaming audiences to SiriusXM’s 25 million subs (who mostly listen within a “captive” environment, i.e., cars – another bad behavioral assumption), just remember that SXM used to be two competing services – Sirius AND XM Satellite Radio – who were forced into merging by near bankruptcy and then had to vigorously argue their case to the U.S. Department of Justice that a satellite-radio monopoly wasn’t anti-trust in nature but literally the only way that U.S.-based satellite radio could even survive. (And now with five separate major lawsuits over pre-1972 copyrights coming from all of the major labels, SoundExchange – who is also alleging manipulation of gross revenues, as well as underpayment of royalties – and of all things, The Turtles, SiriusXM is in for a serious world of hurt. Yet, not only could an adverse judgment in this matter be devastating to their business, it could be devastating for all pre-1972 music rights users, since those particular rights are governed by state regulations and not federal copyright regulations.)

As is, Pandora’s 3.18 million paid subscribers produce 20% of its gross revenue – a very small audience portion when compared to its 73.4 million active listeners using their free ad-based service (4.33% of total listeners). Spotify, showing clear signs that it’s also looking at going public, continues to emphasize its subscription-based service as their primary model – repeatedly using the target number of 40 million potential subscribers as a way to illustrate the amount of money it could contribute to musicians; as of December, however, Spotify’s subscription figures hover at around 6 million worldwide subscribers out of 26 million users…

Yet, even while emphasizing their subscription models, both services are rapidly intensifying their efforts to boost ad-based revenue; in fact, Pandora is now aggressively courting terrestrial broadcasting’s Golden Calf – the polarizing world of political advertising.

According to a report by Elizabeth Dwoskin of The Wall Street Journal, Pandora just introduced a new advertising service designed specifically for political candidates and organizations. Pandora has allowed political advertisers access to their ZIP-coded user base since 2011, but this new service will now fine tune their targeting of an individual listener’s political leanings by tailoring their previously held ZIP code information to specific election results – then combining those results with a comparison of an individual user’s musical tastes to those found more frequently in Democratic or Republican areas, thus identifying the user’s political affiliation (and making Pandora’s pool of individual user data far more valuable). Moreover, the service’s free listeners won’t be able to opt out of Pandora’s political ads – at least not without a subscription. (This sounds like an outstanding way to piss off your listeners.)

So, while still struggling to widen their discovery/recommendation options to increase long-term user engagement and subscription base, more and more services are slamming into the brick wall of short-term-revenue-growth expectations from investors and adopting a more radio-like approach to streaming – all in a desperate attempt to show any essence of profitability.

But shifting to a radio-like approach is not a long-term winning formula: in a white-paper titled “The Future of Legacy Media” (the executive summary is free), Borrell Associates has made the observation that AM/FM TSL – time-spent-listening – has dropped 30% between 2008 & 2013, possibly traded to online services; so, we’re talking about a finite market, not an expanding one.

Meanwhile, in another report from The Wall Street Journal, terrestrial radio’s response to streaming is to actually tighten their playlists – offering less variety than ever – based on a growing volume of research revealing terrestrial radio listeners “tune out when they hear music they don’t recognize.” This is disastrous news to labels and mainstream musicians, as it makes it even harder to launch singles, much less sell albums… and independent musicians? To terrestrial radio, they just don’t exist.

And the news for subscription services just keeps getting worse. According to a December 2013 report by MIDIA Consulting delving into the behavior of British consumers (according to MIDIA, “one of the most digital markets”), when looking at “the entire base of consumers that have either previously been subscribers, currently are subscribers, or plan to become one, 44% have either already churned” – that is, have dumped their subscriptions – or “plan to do so.” That’s a huge figure.

The growing fracturing of available music rights – that is, exclusive music rights available through one service outlet – might seem to serve a music service’s competitive advantage in fending off user churn… but it could also actually work to deter subscription. Again, it’s counter-intuitive to human behavior. What advantage is there to a subscription if a listener has to go to several different services to hear what they want? And with Apple now reportedly pushing labels for more exclusivity and/or windowing deals – denying streaming services access to new music for an indefinite period in an effort to bolster download sales – the subscription model for casual listenership becomes even more likely to falter; in a tight economy, very few listeners will subscribe to multiple services and instead look for free options only.

And as technology advances, those consumers who do not view music as a high priority will be offered a parade of many new and engaging entertainment options other than faddish music services (remember Turntable.FM) and thus shift the focus of the unengaged casual music audience.

Now, mix all of the above together with ISPs and mobile providers tightening their broadband caps as a method to tie consumers to sponsored partnerships and legacy models, and to discourage users from adopting third-party unsponsored options…

Then add in those new rising rates for music rights – soon 60% to 70% payouts of gross revenues will seem quaint…

Fold all those factors together, and it’s hard not to foresee an industry shakeout of immense proportions rolling down the highway – a perfect storm that leaves behind three major non-music companies who will have only a periphery interest in music as it relates to their core businesses; Bandcamp, who, due to their niche model, should weather the storm well; and maybe, if they’re lucky and they don’t disappear entirely, one or two standalone music-streaming services, desperately fighting to maintain break-even revenue models.

This shakeout won’t even be fractionally as big as the 1999-2001 bubble: when you exclude Pandora’s current stock-managed market cap hovering above $6 billion (Pandora’s market cap peaked on March 5th at $7.66 billion, and has been falling rapidly since then), the total capitalization for all music streaming services is now estimated to be just a little over $1 billion – but it will still deliver a devastating shock to the music ecosystem.

And where will that type of shakeout leave musicians – even legacy musicians?

Looking at no discovery and worse, no corporate incentive for creating discovery; a pre-shrunk buyers/users base; and smaller payouts to artists than they are seeing today, even with an increased percentage royalty rate. (Any percentage of zero is still zero.)

Legacy musicians will be in an even worse situation: the only bright spot in 2013’s sales reports showed current artists’ gains of 3.5%, while catalog sales of older artists dropped even more severely than those of more recent musicians.

And if you think that a collapse of these services will bring about a sudden revival of download purchases (oops, don’t forget those broadband caps) or physical product sales, just remember the top number in the Emap study – the 42% that said they “would not lose much sleep if music ceased to exist” – and then add in the number of disaffected listeners that such a massive collapse might generate. (Never underestimate the power of a pissed-off public.)

As sales and royalty payouts plummet, the market will tighten and stagnate, perhaps not immediately (remember what we said about change occurring slowly) but eventually.

Welcome to the Future.

Pretty grim shit but again, entirely possible. And sure, there are lots of other possibilities, but very few of them have positive outcomes.

This is why creating some form of open, active community-based music discovery platform is now vital,  not only for the many positive reasons we’ve mentioned earlier but also as a protective foundation or buttress against the fallout that may be coming. If all the above events come to pass, a revitalized, enthusiastic support base for musicians and listeners will go a long way toward rebuilding a thriving music ecosystem faced with a post-streaming future.

So, Here We Are, But Where Do We Go?

So… faced with all these obstacles in our way, how do we move forward?

We don’t know.

That’s why we wanted to get you to think about this problem – to get you to see the elephant in the room – and perhaps to get as many people from musical and technological disciplines to think about how we can all work together to come up with viable solutions to beat this nightmare before it happens.

All that we do know is this:

• Right now, there are thousands upon thousands of incredible musicians making wonderful music that you’ve never heard – and most likely will never hear – due to the lack of a truly open discovery methodology that would enable you to know that their music even exists. They don’t have promotional budgets; they don’t fit into established genres; and if they’re lucky, they may scrape by because of listeners who have found their music and passed it on via word of mouth.

And sure, they’d love to make music on a full-time basis, or even just make enough to pay a few of their bills, but without an open, non-weighted discovery methodology, or a social community that can serve to recommend them to other listeners, they will never be heard.

• Recommendation has to become more accurate and trustable, with no hidden agendas or special rights deals weighting the results; it has to become available as something that’s cross-service and cross-platform; and it has to be all-inclusive, across all music – not just addressing one particular silo of rights bases.

• The true solution won’t be found in cannibalizing extant, mainstream, casual-listening audiences who have no interest in paying money for a service and therefore might listen to a stream that is populated with a growing wall of advertising – but instead through developing an engaging methodology that would actually grow the base of active listeners and act as a methodology to encourage others toward active listenership.

• Building a truly all-inclusive, active-community recommendation platform would go a long way toward those goals:

– by producing explicit real-time, real-person data and not relying on implicit probabilities, it would significantly increase the signal-to-noise ratio and fine tune filtering and recommendation capabilities;

– by building it as a platform, with an open API, developers could continue to expand on and future-proof its capabilities and feed them across services in a full-duplex or interactive manner, while staying independent of those services;

– and most importantly, by building a base of active listeners that would continue to grow as more listeners discover the joy of being active within a new and vibrant community, thus providing the music world with a foundation and bulwark against a shakeout that may already be in progress.

We’re talking about creating a scenario where everybody wins – not just winners and losers.

It could be done as a for-profit operation; it could be done as a giant cooperative effort amongst all parties; it could even be done as an open academic platform that all information and music services could pull data from to use in their proprietary algorithms, yet remain free to all – one massive library of music data that could encompass the vast amount of music already created and still be scalable for the music that is yet to be created; hell, if you wish to be grandiose, it could even be a project for the ages…

In fact, it could be done in any number of ways not even mentioned above.

But whatever method it takes to create it, it needs to be done, and it needs to be done now, before it’s too late and we all get our collective asses handed to us by The Future…

… because if we simply do nothing and think only of ourselves and our own interests, and thus allow things to progress in the direction they’re moving, that day is surely coming.

And that’s our biggest problem: nobody seems to be thinking about music as just Music anymore; and nobody is thinking about the listener as an individual, a fellow human being. Everybody’s looking out for their proprietary interests, all desperately grasping to hang onto or get a hold of a piece of the pie, promoting their own agendas by spewing massive levels of toxic discourse within think pieces and press releases and mass emailings and panels and seminars about streaming vs. sales and royalties and rights – all of it poisoning the very ecosystem of music and immersing listeners within a sea of negativity… with even your most active listeners believing that you really don’t give a damn about them: all you want is their money.

And therein lies the real danger: if you don’t care about your listener as an individual – if they think that all they are to you are anonymous sets of ears and eyeballs attached to wallets – then why the hell should they give a fuck about you?

This is the truly toxic effect of the Elephant in the Music Room: it isn’t just the abundance of music or our lack of ability to penetrate this immense abundance – it’s our desperate, fearful, self-interested reactions to this abundance and the technologies surrounding it. Whether you like it or not, we’re all in this together – divided, we fall – and right now, we’re collectively setting ourselves up for a fall of gargantuan proportions.

If we truly wish to stop this fall, we need to pull together as yet another community – a community of musicians, technologists, writers, entrepreneurs, and listeners – actively working in a positive manner toward creating solutions and common ground so we might find mutually beneficial answers to all the important questions that currently divide us, and allow us to develop truly sustainable economic models for everyone.

Music is Music. It’s not “content,” nor a siloed commodity awaiting better methodologies for monetization…

Music is our primal, communal language; if you really need examples of that, go look at some of the videos from the Singing Wells project, who are working on archiving the traditional music of East Africa…

… or better yet, watch this video for Landfill Harmonic, where the people of Cateura, a slum community built on top of Paraguay’s biggest landfill, eke out a living picking through the 1,500 tons of solid waste sent to them daily; yet, in picking through all that garbage, they find reusable bits of cast-off junk that – through sheer ingenuity – they transform into classical instruments… to give the gift of music to their children.

Around 2:26 into that short video, a young Cateuran girl sums it up best:

“My life would be worthless without music.”

That’s the power of music.

Music is joy. It is sorrow. It is a visceral celebration of life itself.

Most importantly, music defines who we are as People: it is our humanity. That’s why it’s so very, very precious.

And that’s why we wrote this article.

So, yeah. There’s an Elephant in the Music Room.

And the time to do something about it is running out. Or, to quote Eno again: “Sorry, mate. History’s moving along.”

We see only two options:

We can all choose to see this elephant, pull together as a community, and find methods to deal with it…

Or we can just continue to bitch, moan, complain, think only about our own interests and egos while demonizing and beating the crap out of each other, drive away our listener with mountains of negativity, and continue attempting to live in the past while feverishly grasping at smaller pieces of that ever-diminishing pie until the whole shit-house comes down right on top of us all.

Act with change – or become a victim to it.

The choice is yours.

~

another cultural landslide are a pair of troublemakers who have released music & caused trouble for over 12 years. You can find their most recent music here; or if you just want to yell at them, they’re on Twitter at @anothercultlandTheir manager is a cat. (mzkitty sez hi)

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