Tag: playlists

One billion music creators: what does that look like?

Posted on | by Maarten Walraven-Freeling

Making a tune is now as easy as taking a photo and uploading it to Instagram. With its 1 billion monthly active users, Instagram has made photographers out of all us. As phone cameras improved, the Instagram filters did the rest. Music has spent most of this century battling the ghosts of piracy. The major labels first reinvented themselves as licensing models, using the IP in their catalogues to generate revenues that has propelled them back to pre-Napster times. More recently, everybody in the industry has become a digital media company: Warner invests heavily in gaming, musicians like Rihanna and Jay Z build out their brands far beyond music, and every indie artist has to navigate the digital world from social media to direct-to-fan strategies. Alongside these developments music creator tools proliferate. From Myspace to Soundcloud and now TikTok, sharing and discovering music has undergone changes in relation to the underlying medium. Each new medium led to new structures in pop music. Now, the next step is that everyone can become a music creator and it won’t be long before we see a medium where we consume music as readily as we adapted our thumbs to scroll through miles of photographic content each year.

Creator tools

In November 2020 MIDiA Research already claimed that creator tools were the present of the music industry. They showed that there were 14.6 million music creators using apps and platforms like YouTube and Soundcloud, but also Vampr, Bandlab, Loopcloud, Splice, Boomy, and many more.

These tools range from a focus on distribution to collaboration and various stages of production like loops, mastering, and effect. If you look to collaborate in real time, you can. If you want to easily get your track mastered there’s no need to go to a studio. If you need a specific sound, there’s now any number of sample libraries you can turn to.

That number of 14.6 million calculated by MIDiA is sure to have grown in the past year. Similar to the number of tracks released on the streaming services, roughly 60,000 per day back in February, which will also keep increasing. If you look at an app like Bandlab, you see the potential wave of content generated through creator tools: users create 11 million tracks each month. That’s the total number of tracks released to streaming services over a period of 6 months. In other words, not all of those tracks are released. As these numbers continue to grow, the music industry will need to change to adapt.

How to stand out from the crowd? Or should you even aspire to it?

It’s already difficult to make sure you get your new track heard when you are one of 60,000. If you are an indie you may never get the chance to make it onto those New Music Friday playlists. A study released earlier this year shows how “independent label artists are getting far less than their fair share of access to the most popular playlists.” This kind of issue underlines the current power structures in music. But if we transpose the number of tracks currently released on Bandlab in a month to the DSP format, it could well be that that whole system would simply break down. Moreover, if we take the step to consider a billion music creators using an app like Bandlab each month the volume of created music would be close to unimaginable.

In a way, this is happening already. TikTok has more than one billion video views each day, and most of those include music. Snap, through it’s acquisition of Voisey, now has millions of users creating tracks each day to add to their snaps. And yet, there’s no dedicated medium for music yet that has attracted these one billion MAUs. Once we get there, though, it won’t be about standing out from the crowd of recorded music anymore. Instead, each creator will be subject to the same things anyone on a current social media-platform is: algorithms and community. It’s in the latter that we may see a different effect of music growing to the size of photography.

Since music is inherently collaborative, it means that all those creator tools also have these features built into them. Some of them in a very direct way, such as Vampr, others more indirectly, like Splice. But any medium looking to tap into people’s deep-seated desires for making music will have to build to cater to niches. As content will get churned out at an ever greater number people will find each other in shared loves of musical nooks and crannies and find ways to express their identities in relation to that.

Existential fright

Going back to the comparison with Instagram and photography we can use the development of commentary on the medium and digital photography more broadly to sketch responses to a billion music creators. 10 years ago the journalist and artist Chris Wiley wrote that:

“It is indisputable that we now inhabit a world thoroughly mediatized by and glutted with the photographic image and its digital doppelganger. Everything and everyone on earth and beyond, it would seem, has been slotted somewhere in a rapacious, ever-expanding Borgesian library of representation that we have built for ourselves. As a result, the possibility of making a photograph that can stake a claim to originality or affect has been radically called into question.”

So, in a way we’re moving into a world that’s thoroughly mediatized by the sonic in the form of melodies, beats, hooks. Some of these put together by people calling themselves artists, others by people who quickly threw together a few loops. The former might be looking to make a living from their art. The latter might just be enjoying themselves and have no ambition to share their creations beyond a few friends and like-minded people. The question of originality remains pertinent.

That question, however, isn’t new to this situation. Pop music simply doesn’t exist beyond a limited number of chords. And we’ve seen it before, of course. The advent of radio was thought to kill live music consumption – it didn’t. TV was then the death of radio – instead radio revenues increased. The music video would then kill the radio star – radio revenues increased again. The internet doomed the recorded music industry – and piracy had a serious impact, but revenues are now back to where they were 20 years ago. With each new medium, each new iteration of distribution, musicians kept creating and finding audiences.

Final note

Just as smart-phone cameras and Instagram filters have influenced a generation of photographers, so will the current boom of creator tools shape the sound of music for the next decade or so. It’s simply not necessary anymore to have any musical training in order to create music. Apps like Boomy allow everyone to play around intuitively and create sounds that feel like music. Similarly, Bandlab has a loop feature that allows anyone to create something with a pleasant enough melody. Should that lead to existential questions about what music is? Probably not. Instead, we would do well to focus on new niches popping up around shared interests in certain stems, riffs, drum rolls, etc. We may look at and listen to music differently if everyone can make it, but we won’t enjoy it less.

Smarter Playlists: automate your music discovery, playlist strategy, and library organisation

Posted on | by Bas Grasmayer

Smarter Playlists is still the best way to ‘automagically’ create and update playlists on Spotify. The tool, made by Paul Lamere of music data firm The Echo Nest (now Spotify), provides you with countless ways to source music, combine it, filter it, sort it and turn it into playlists.

I hinted at the value of Smarter Playlists / Playlist Machinery when I wrote about playlist strategy in a previous post titled If you want to start a music brand, don’t wait until the pandemic is over. Here’s how to use it.

Music discovery

Not everyone needs a playlist strategy, but everyone reading this is crazy about music and always curious to explore more. Here are some examples of recipes that surface gems.

New Music Friday… but high-energy from around the world

Fridays are when new music is released and Spotify helps surface that new music in numerous ways. It has its the algorithmic Release Radar which lets you listen to tracks from artists you personally follow. It also has New Music Friday playlists for specific territories that are editorial and mostly pop-focused.

I love seeing how trends emerge and are adopted around the world and have a soft spot for high-energy music, so I created a weekly tool to scout new tunes.

A lot happening in this screenshot, so let’s break it down by steps.

Firstly, all of the data streams in from the left and streams out (to a Spotify playlist) on the right. In between, there are various steps which either combine data (e.g. tracks from different playlists), filter, or sort it.

  1. First I added a number of Sources. The Sources are Spotify’s New Music Friday (NMF) playlists from various regions. You copy the playlist URI and add it to the box. I’ve changed the box names to the region it’s sourced from.
  2. Since the international NMFs also tend to feature the world’s biggest pop stars, who I’m already familiar with, I took the global New Music Friday playlist (which has over 3M followers) and connected it to the mixer with a red line. This ‘bans’ all the tracks on the global NMF playlist and essentially filters out the global hits from progressing down the workflow, in case they’re present on any regional playlist.
  3. Since I’m working with 7 input sources, I set the mixer’s max tracks to a few thousand. Otherwise it clips to a low number by default.
  4. It’s Friday – I want energy (tbh, I always want energy). So I took the energy filter and set it to ‘most energy‘. This filters out all tracks that are not energetic.
  5. Next, I’ve sorted the stream by artist popularity and picked ‘reverse’, so that the most popular artist shows up on top of the list. This is counter-intuitive, but it makes sense if you dive into how they rank artist popularity numerically. I do this, because if people visit the playlist and play track 1, it makes it more likely it fits current trends and expectations and people are less likely to move on to another playlist.
  6. But life shouldn’t be too predictable. So I’ve used ‘weighted shuffle‘, which lets you set the degree to which you want the list to be randomized. If you want things to be roughly in order of popularity, you set it to 0.1.
  7. In the above recipe or formula, I save the output to an existing Spotify playlist in my collection. I’ve chosen to overwrite, but you can also select to append. Additionally, you can choose to create a new playlist altogether.
  8. Hit the play button to run your workflow, check if the output makes sense in the Tracks tab and also check your Spotify library for the playlist.

👉 Playlist | Program

I’ve used the scheduler to update it weekly, because I was happy with the result and I imagine I can build a following with the playlist. You can find the scheduler by going to the Program section after saving your playlist recipe.

Scout labels’ playlists for unknown talent

Labels usually have regularly updated playlists which showcase their new releases. If you’re curious about musicians that are less well-known, you can set a filter that removes all tracks by artists that are too ‘popular’ (according to Spotify) for your taste.

The above example features 3 prominent drum & bass labels and is set to append less well-known artists’ tracks to a playlist on a weekly basis. (for the connoisseurs: some of the artists in the playlist are indeed quite legendary, but somehow don’t index high on Spotify’s popularity scoring)

👉 Playlist | Program

Playlist strategy

This toolset is also excellent for simplifying the work that goes into maintaining playlists one might use to build their following. Here are two examples.

Sourcing scene playlists for fresh music

Let’s say you read my recent post and are now building a new music brand. You already have a feeling of what it should sound like and are familiar with popular & less popular playlists in your scene. Your flow might look something like this:

I’ve added red dots to the playlist boxes to make it clearer which is which. In the big group, I have 8 different playlists (Wixapolo, Hardtekk, Lobsta B, Clubland, Pumping, Makina, Hard Dance Interpretations and an old playlist I no longer update) that get randomized and duplicate tracks removed before the mixer picks 50 tracks from them.

I’ve split 3 playlists from that path. For Lento Violento, I want to limit the amount of tracks that may show up, so the mixer on the left is set to a very low number, so only a couple of tracks enter the pool. For the Hyperpop playlist, I only care about the high BPM tracks that may be in there. Lastly, there’s trash rave, which is a big pool of music I add music to regularly. I want this playlist to dominate the flavour of the final playlist, so I’ve seperated it, so I can make sure the mix from the 10 playlists on the left have about a 50/50 ratio to the trash rave playlist.

Artist separation makes sure the same artist doesn’t appear multiple times in a row.

Enjoy some of the goofier bpms of dance music.

👉 Playlist | Program

Turn one big playlist into daily instalments

Let’s say you’ve been collecting loads of music into one big playlist, but you want to turn that into a highly engaging format that people come back to daily. This one is really simple.

For years, I’ve been compiling various types of Club Music into one big playlist – from Jersey Club to Juke to Bmore, you name it. Let’s turn it into a brief playlist that people can come back to daily.

Shuffle the input, so you don’t end up with only the top tracks (update: in this case, the ‘sample’ selector does the same as ‘shuffle → mixer’). Remove duplicate artists, since it updates daily, so keep it varied. That’s it. Don’t forget to set it to update every day via the clock icon in the Programs tab.

If you’re ready to move, give the resulting playlist a listen.

👉 Playlist | Program

Library organisation

Not everyone’s building playlist brands, but you may have a library that could use some organisation.

‘Focus music’ playlist based on what you know

This one was shared by Antoine Marguerie, a designer at Base Secrete.

I’ve rebuilt it and it takes familiar music (less distracting), filters the stream to only include low BPM tracks, removes some duplication, removes any lyric-heavy tracks, and takes a 100 tracks to add to the focused work playlist. A good way to reconnect with music you’ve already discovered.

For me, the result still requires some fine-tuning, because sometimes Spotify gets the BPM wrong and thinks a 160 bpm track is 80 bpm. This may not be an issue for most people, but my music taste makes those false positives quite likely to appear in my library. You could address that with energy and danceability filters.

👉 Program

Cleaning up a playlist with lots of albums

One of Spotify’s strengths is the convenience with which you can build playlists. Just drag and drop albums into a playlist and you’re done. The result is a playlist with albums all grouped together. In case you don’t want that, here’s something you can do.

This takes an unorganized source playlist, puts the most popular tracks towards the top and then shuffles things around with ‘weight’ (meaning you can set how random you want things — less random preserves the rough order of the list). In this formula I sent it to a new playlist, because I wanted to hold on to the source playlist.

👉 Playlist | Program

Your turn

The Smarter Playlists has FAQs and many additional examples. Start playing around and think of how you may put this to use. By automating, you’re programming, since this tool is a lot like a visual programming language. You can drop your programs in the comment section below, or drop them in this Twitter thread. Don’t forget to make backups in case you’re overwriting playlists.

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The Chris Brown problem on Spotify

Posted on | by Bas Grasmayer

How do we deal with bad players in music when every listen translates to payment?

For a few weeks in a row now, Chris Brown has appeared in my Spotify Release Radar. I’m not sure why, because I don’t follow him, nor do I really listen to similar music, but that’s a different topic.

The issue I have is: I do not want my streams to put money into the pockets of abusers (Chris Brown has a history of violence towards women, and victim-blaming). So that means I can’t really listen to my Release Radar in the background, or most curated playlists for that matter, because I want to make sure Spotify never plays those tracks to me.

I’m singling out Spotify here, because I’m an avid user: basically all streaming services have this problem. I’ve made the case for a global ban button for particular artists before, when I wrote about the Moby problem on Spotify. Basically, in curated environments, it would be nice to give some control back to the user and let them blacklist certain artists they’re not comfortable with.

Not only would this give listeners a more manageable overall experience, but it would also allow people to immediately make sure their money doesn’t go to abusers (and in the aftermath of the Weinstein fallout, surely Hollywood’s revelations will start spreading to the music business too).

But there’s another issue: in the streaming era, how do we listen to controversial artists without sending money their way?

For example, some brutal details around rapper XXXTentacion came out a while ago. He comes across as an abusive monster, and regularly gets into fights with fans. Yet, he’s still very popular. I’m curious why – is the music that good? I opened Spotify to check him out, but stopped myself from hitting the playback button, being aware that listening means money will go towards him (or his label, and seriously, they should do a Netflix and drop this dude + donate profits to causes that help victims / survivors of abuse).

But the ‘Chris Brown problem’ is that dudes like this keep being put into popular playlists, keep appearing in users’ personal playlists through algorithm recommendations. As listeners, we need a way to shield ourselves, and prevent our money from going into the pockets of these people.

If Spotify and other services are serious about their passive ‘lean back’ experience: give us a blacklist button. Let us ban Chris Brown.

Meanwhile one Reddit user has a suggestion for when artists you like collaborate with such people (which I’m sure a lot of readers won’t like):

Comment from discussion Snoop Dogg's new album cover features him standing over Trump's dead body.

What the End of the App Era Means for the Music Business

Posted on | by Bas Grasmayer

The average smartphone user downloads less than 1 app per month, according to comScore. The era of apps is ending, and we’re moving in an era of artificial intelligence interacting with us through messaging apps, chatbots, voice-controlled interfaces, and smart devices.

What happens to music in this context? How do you make sure your music stands out? How do you communicate your brand when the interface goes from visual to conversational? And what strategic opportunities and challenges does the conversational interface present to streaming services?

 

How can we restore music’s status as social glue in the age of streaming?

Posted on | by Bas Grasmayer

The case for a passive discovery mechanism for friends’ playlists on Spotify.

This article started with a tweet on a Saturday evening. Simply put: I wish I had a better interface to discover playlists that are popular among my friends.

Mark Newman rightfully pointed out that Spotify doesn’t show much interest in surfacing user-created playlists. As a matter of fact, they have even been deemphasising them over the years. Instead they opt for sending people to their own playlists. And their priority makes sense. They have to compete with giants like Apple, Google, Amazon: companies that have money to waste, while Spotify has money to raise.

Streaming is going mainstream

I’m sure to most of us it feels like it’s mainstream already. Hear me out.

Spotify, and other streaming services, are now focusing on consumers beyond the early adopter. These are people that are happy listening to the hits from the radio. These are people that like predictable music experiences. And they’re the bulk of the market.

In order to successfully compete for them, streaming services have to deliver very consistent streaming experiences to these people. This comes in the form of speed, functionality, but also content and programming.

User-created playlists fall outside of Spotify‘s editorial guidelines and metrics that they set for their editors, so it makes it unpredictable. Then again, features like Discover Weekly carry some inherent unpredictability with them: it’s what makes them fun and addictive.

The metrics that a feature like this probably needs to deliver on would look like:

  • Amount of time spent listening to music on Spotify in a specified timeframe (the feature should not lead to less playback);
  • Some kind of retention metric (should lead to a more engaging product, with less people stopping to use it).

Spotify’s friend activity & navigation

I like seeing what my friends are listening to in the right hand bar. Occasionally, but hardly ever, I click on something someone is listening to, and musically stalk my friend.

The reason why I hardly ever tune into my friends that way, and why I think it’s probably not an often-used feature, is because you tend to see it when you’re already listening to something. It’s not really positioned inside the product as a starting point; it’s more of a distraction.

Starting points, in Spotify, are either search or are presented in the left-hand menu. They are your playlists, or the other navigation points, such as podcasts, browse, and Daily Mix.

The prominent placing of Your Daily Mix stands out to me. I find the feature a bit dull and repetitive, but perhaps that’s because I’m on the end of the user spectrum that explores more than returns to the same music. The point is: Spotify gives prominence to an algorithm that generates 5 daily playlists for users. It’s somewhat unpredictable, compared to what they feature in Browse, but it tries to get people into a daily habit, and its prominent placing suggests that this may be working.

What should also be noted is that none of these navigation items include anything social, despite the entire right-hand bar being dedicated to it.

Browse is boring

I’m always disappointed when I open the Browse tab. I never really see anything surprising and I keep seeing the same things over and over, despite not engaging with them.

There are so many super interesting playlists on search, particularly those by third parties, and I need a way to surface them without finding out on curators’ websites, social media, by using search, or by visiting artist profiles.

Your Daily Friend Mix

So, back to my original tweet, and the requirements for getting a social feature to work well:

  • Should lead to people regularly coming back;
  • Should lead to increased playback (or at least no decrease).

What are the constraints?

  • Not enough friends to meaningfully populate an area;
  • Friends don’t listen to playlists;
  • Friends only listen to the same playlists as you;
  • Friends’ tastes are too dissimilar.

The first issue here is already tackled by the way Spotify handles Discover Weekly and its Daily Mixes: if they don’t have enough data on you, they won’t present these features to you. So in short: if there’s not enough useful data to present meaningful results to you, the feature should not be shown.

However for many users there would be meaningful data, so how to make sure that the suggested content is also meaningful?

The UX of recommendations is a big topic, but in simple terms, there should be thresholds and ceilings on similarity:

  • Recommended content should not have a similarity higher than 90% to user’s collection;
  • Recommended content should not have a similarity lower than 10% to user’s collection & listening history.

The recommended content can be playlists made by friends, or ones that friends regularly listen to and / or are subscribed to. The percentages are made-up, and there are a lot more things you could factor in, but this way you make sure that:

  1. Content in the section is interesting, because you’ll discover something new;
  2. And it’s not too random or too far from your taste, so you’ll always find something you’d want to listen to while opening the section.

If that’s taken care of, then people will keep coming back. Why?

Because it’s super fun to discover how your taste overlaps with friends, or to discover new music with friends. I also think such a feature would work better for Spotify‘s demographic than the more active one-on-one music sharing type of functionality (that Spotify removed recently).

Spotify needs a passive way to connect with music through friends

The messaging functionality that Spotify removed showed low engagement. That’s because music one-on-one recommendations are demanding on both sides. Instead, what has shown to work best on big streaming platforms, are lean back experiences. Discover Weekly is an example of that: it’s focused on the result, rather than the action. The action for discovery is exploration: with Discover Weekly, it’s Spotify‘s albums that do most of the exploring for the user.

That’s what the social side of the service needs. The Friend Activity feed is boring. It hardly ever shows something I’d like to listen to, but I do know my friends listen to music I’d be interested in…

What I need is a section that I can go to when I’m looking for something new to listen to, and then shows friends as social proof for that content. It allows me to connect to friends in new ways. Perhaps even strike up a conversation with them on Facebook Messenger.

Which would pair well with Spotify‘s strategy to drive more engagement through Messenger.

AI-created non-human music will need human narratives

Posted on | by Bas Grasmayer

To me, it’s beyond a doubt that we’ll all be listening to AI-created music within a few decades, and probably much sooner. The most important way in for this type of music is mood playlists. After the first couple of songs on such playlists, most people tune the music out and get back to their main activity. Does it really matter who has created the song then? Does it matter whether they’re alive? Does it matter whether they’ve ever been alive at all?

[EDIT Aug 15: a small disclaimer since a piece linking here makes an incorrect claim. I don’t think all AI-created music needs a human narrative. I believe the future contains a lot of adaptive, and generative music. More on my point of view in this piece: Computers won’t have to be creative]

We are all creative, and therefore I think it doesn’t matter whether computers will be able to be creative. We are creative as listeners. Computers will be able to predict what we like, then test thousands of versions on playlists until they have the exact right version of the song. As a matter of fact, AI offers the prospect of personalized music, or music as precision medicine as The Sync Project calls it.

A point that’s made often is that AI-created music lacks part of the story people expect with music. People bring it up as an obstacle that can’t be overcome, but it feels like that’s just because of a decision to stop thinking as soon as the point is brought up. Let’s think further.

For one, I think AI-created music already is and will continue to be born in collaboration with people. People will increasingly take the role of curators of music created through algorithms. Secondly, why not give music a story?

Last week at IDAGIO Tech Talks, the music streaming service for classical music where I’m Product Director, we had the pleasure of hearing Ivan Yamshchikov talk about his neural network capable of music composition. With his colleague, Alexey Tikhonov, they fed their system 600 hours of compositions and had it compose a new work in the style of Scriabin. The human narrative was added at the end: as it was performed live by acclaimed musicians (see below).

This is how you get people to knowingly listen to music by artificial intelligence. Most consumption of AI music will be through ignorance of the source of the music. Yet people will warm up to the idea of AI being involved in the music creation process, just like they warmed up to electric guitars, samplers, and computers being used as instruments.

And that’s the narrative that will make it human: artificial intelligence as an instrument which requires a whole new skill set for artists to successfully work with it, and evoke in listeners what they want to.