Posts Tagged Music
A bunch of music tech folk will be in Dublin Ireland next week to attend ACM Recommender Systems 2012. We’ll be heading over to the Bull & Castle, beside Christ Church, Dublin City on September 13 at 18:30 to join <Pub> Standands Dublin, to hang out and chat about hacking music. Pub Standards is a post-conference drink-up without the conference. There’s no format, talks or presentations. It’s just geeks + beer. If you are in the area and are interested in hanging out, feel free to come on down and have a beer.
I’ve received quite a bit of feedback on my recent Most Musical City post, especially from folks from Austin that didn’t like Austin’s 14th place ranking. This reddit/austin comment thread was rather brutal, and this Austinist article Wait, What?! Austin Not Ranked In Top 10 Musical Cities List even closed with this appeal: any data analysts out there up for the challenge to get Austin closer to the top?
Well, John Rees, the Director of Community & Economic Development at Capital Area Council of Governments in Austin is just the data analyst that the Austinist was looking for. He re-ran the analysis but instead of using city populations he calculated the rankings based upon metropolitan statistical areas. In the May issue of Data Points Newsletter John reports on this analysis:
When data from The Echo Nest is adjusted to include metropolitan statistical area population data, the rankings of America’s most musical places changes significantly. Topping the list is Nashville, San Francisco and Los Angeles (which includes Beverly Hills). The Austin region jumps ten places from the original list to become America’s forth [sic] most musical region.
John goes on to point out some of the non-quantifiable aspects of the Austin music scene such as the diversity of music as well as the presence of events such as SXSW and Austin City Limits. John makes a strong argument that Austin is one of the country’s premier music destinations. Even the reaction of Austin’s residents to my post says a lot about Austin as a music city. People from Austin really care about music and don’t take it kindly when they are not at the top of the most musical city list. So congrats to Austin, not just for moving up the chart but also for demonstrating that Austin is the city that is most passionate about music
Lots of ink has been spilled about the Loudness war and how modern recordings keep getting louder as a cheap method of grabbing a listener’s attention. We know that, in general, music is getting louder. But what are the loudest songs? We can use The Echo Nest API to answer this question. Since the Echo Nest has analyzed millions and millions of songs, we can make a simple API query that will return the set of loudest songs known to man. (For the hardcore geeks, here’s the API query that I used). Note that I’ve restricted the results to those in the 7Digital-US catalog in order to guarantee that I’ll have a 30 second preview for each song.
So without further adieu, here are the loudest songs
The song Topping and Core by Grmalking555 has a whopping loudness of 4.428 dB.
The song Modifications by Micron has a loudness of 4.318 dB.
The song Hey You Fuxxx! by Kylie Minoise with a loudness of 4.231 dB
Here’s a little taste of Kylie Minoise live (you may want to turn down your volume)
The song War Memorial Exit by Noma with a loudness of 4.166 dB
The song Hello Dirty 10 by Massimo with a loudness of 4.121 dB.
These songs are pretty niche. So I thought it might be interesting to look the loudest songs culled from the most popular songs. Here’s the query to do that. The loudest popular song is:
The loudest popular song is Welcome to the Jungle by Guns ‘N Roses with a loudness of -1.931 dB.
You may be wondering how a loudness value can be greater than 0dB. Loudness is a complex measurement that is both a function of time and frequency. Unlike traditional loudness measures, The Echo Nest analysis models loudness via a human model of listening, instead of directly mapping loudness from the recorded signal. For instance, with a traditional dB model a simple sinusoidal function would be measured as having the same exact “amplitude” (in dB) whether at 3KHz or 12KHz. But with The Echo Nest model, the loudness is lower at 12KHz than it is at 3KHz because you actually perceive those signals differently.
Thanks to the always awesome 7Digital for providing album art and 30 second previews in this post.
Yesterday, SXSW opened up the 2012 Panel Picker allowing you to vote up (or down) your favorite panels. The SXSW organizers will use the voting info to help whittle the nearly 3,600 proposals down to 500. I took a tour through the list of music related panel proposals and selected a few that I think are worth voting for. Talks in green are on my “can’t miss this talk” list. Note that I work with or have collaborated with many of the speakers on my list, so my list can not be construed as objective in any way.
There are many recurring themes. Turnatable.fm is everywhere. Everyone wants to talk about the role of the curator in this new world of algorithmic music recommendations. And Spotify is not to be found anywhere!
I’ve broken my list down into a few categories:
Social Music – there must be a twenty panels related to social music. (Eleven(!) have something to do with Turntable.fm) My favorites are:
- Social Music Strategies: Viral & the Power of Free – with folks from MOG, Turntable, Sirius XM, Facebook and Fred Wilson. I’m not a big fan of big panels (by the time you get done with the introductions, it is time for Q&A), but this panel seems stacked with people with an interesting perspective on the social music scene. I’m particularly interested in hearing the different perspectives from Turntable vs. Sirius XM.
- Can Social Music Save the Music Industry? – Rdio, Turntable, Gartner, Rootmusic, Songkick – Another good lineup of speakers (Turntable.fm is everywhere at SXSW this year) exploring social music. Curiously, there’s no Spotify here (or as far as I can tell on any talks at SXSW).
- Turntable.fm the Future of Music is Social - Turntable.fm – This is the turntable.fm story.
- Reinventing Tribal Music in the land of Earbuds - AT&T – this talk explores how music consumption changes with new social services and the technical/sociological issues that arise when people are once again free to choose and listen to music together.
Man vs. Machine – what is the role of the human curator in this age of algorithmic recommendation and music. Curiously, there are at least 5 panel proposals on this topic.
- Music Discovery:Man Vs. Machine – MOG, KCRW, Turntable.fm, Heather Browne
- Music/Radio Content: Tastemakers vs. Automation – Slacker
- Editor vs. Algorithm in the Music Discovery Space – SPIN, Hype Machine, Echo Nest, 7Digital
- Curation in the age of mechanical recommendations – Matt Ogle / Echo Nest – This is my pick for the Man vs. Machine talk. Matt is *the* man when it comes to understanding what is going on in the world of music listening experience.
- Crowding out the Experts – Social Taste Formation – Last.fm, Via, Rolling Stone - Is social media reducing the importance of reviewers and traditional cultural gatekeepers? Are Yelp, Twitter, Last.fm and other platforms creating a new class of tastemakers?
Music Discovery – A half dozen panels on music recommendation and discovery. Favs include:
- YouKnowYouWantIt: Recommendation Engines that Rock - Netflix, Pandora, Match.com – this panel is filled with recommendation rock stars
- The Dark Art of Digital Music Recommendations - Rovi – Michael Papish of Rovi promises to dive under the hood of music recommendation.
- No Points for Style: Genre vs. Music Networks – SceneMachine – Any talk proposal with statements like “Genre uses a 19th-century tool — a Darwinian tree — to solve a 21st-century problem. And unlike evolutionary science, it’s subjective. By the time a genre branch has been labeled (viz. “grunge”), the scene it describes is as dead as Australopithecus.” is worth checking out.
Mobile Music – Is that a million songs in your pocket or are you just glad to see me?
- Music Everywhere: Are we there yet? – Soundcloud, Songkick, Jawbone – Have we arrived at the proverbial celestial jukebox? What are the challenges?
Big Data – exploring big data sets to learn about music
- Data Mining Music - Paul Lamere – Shameless self promotion. What can we learn if we have really deep data about millions of songs?
- The Wisdom of Thieves: Meaning in P2P Behavior - Ben Fields – Don’t miss Ben’s talk about what we can learn about music (and other media) from mining P2P behavior. This talk is on my must see list.
- Big data for Everyman: Help liberate the data serf - Splunk – webifying and exploiting big data
Echo Nesty panels – proposals from folks from the nest. Of course, I recommend all of these fine talks.
- Active Listening – Tristan Jehan - Tristan takes a look at how the music experience is changing now that the listener can take much more active control of the listening experience. There’s no one who understands music analysis and understanding better than Tristan.
- Data Mining Music - Paul Lamere – This is my awesome talk about extracting info from big data sets like the Million Song Dataset. If you are a regular reader of this blog, you’ll know that I’ll be looking at things like click track detectors, passion indexes, loudness wars and son on.
- What’s a music fan worth? – Jim Lucchese - Echo Nest CEO takes a look at the economics of music, from iOS apps to musicians. Jim knows this stuff better than anyone.
- Music Apps Gone Wild – Eliot Van Buskirk – Eliot takes a tour of the most advanced, wackiest music apps that exist — or are on their way to existing.
- Curation in the age of mechanical recommendations – Matt Ogle – Matt is a phenomenal speaker and thinker in the music space. His take on the role of the curator in this world of algorithms is at the top of my SXSW panel list.
- Editor vs. Algorithm in the Music Discovery Space - SPIN, Hype Machine, Echo Nest (Jim Lucchese), 7Digital
- Defining Music Discovery through Listening – Echo Nest (Tristan Jehan), Hunted Media - This session will examine “true” music discovery through listening and how technology is the facilitator.
- Designing Future Music Experiences – Rdio, Turntable, Mary Fagot – A look at the user experience for next generation music apps.
- Music at the App Store: Lessons from Eno and Björk – Are albums as apps gimmicks or do they provide real value?
- Participatory Culture: The Discourse of Pitchfork – An analysis of ten years of music writing to extract themes.
I’ve submitted a proposal for a SXSW 2012 panel called Data Mining Music. The PanelPicker page for the talk is here: Data Mining Music. If you feel so inclined feel free to comment and/or vote for the talk. I promise to fill the talk with all sorts of fun info that you can extract from datasets like the Million Song Dataset.
Here’s the abstract:
Data mining is the process of extracting patterns and knowledge from large data sets. It has already helped revolutionized fields as diverse as advertising and medicine. In this talk we dive into mega-scale music data such as the Million Song Dataset (a recently released, freely-available collection of detailed audio features and metadata for a million contemporary popular music tracks) to help us get a better understanding of the music and the artists that perform the music.
We explore how we can use music data mining for tasks such as automatic genre detection, song similarity for music recommendation, and data visualization for music exploration and discovery. We use these techniques to try to answers questions about music such as: Which drummers use click tracks to help set the tempo? or Is music really faster and louder than it used to be? Finally, we look at techniques and challenges in processing these extremely large datasets.
- What large music datasets are available for data mining?
- What insights about music can we gain from mining acoustic music data?
- What can we learn from mining music listener behavior data?
- Who is a better drummer: Buddy Rich or Neil Peart?
- What are some of the challenges in processing these extremely large datasets?
Flickr photo CC by tristanf
I just fired up my Google Music account this afternoon and this is what I found:
All 7,861 songs are gone. I hope they come back. Apparently, I’m not the only one this is happening to.
Update – all my music has returned sometime overnight.
Peter Sobot (@psobot ) has used The Echo Nest Remix to automatically add dubstep to any song.
The Crash Bandicoot Dubset remix is pretty wild. Peter says that The Wub Machine is still work in progress. Check out how it works and add your ideas to the mix on Peter’s blog.
In a couple of weeks I’m giving a talk at SXSW called Finding Music with pictures : Data visualization for discovery. In this panel I’ll talk about how visualizations can be used to help people explore the music space and discover new, interesting music that they will like. I intend to include lots of examples both from the commercial world as well as from the research world.
I’ll be drawing material from many sources including the Tutorial that Justin and I gave at ISMIR in Japan in October 2009: Using visualizations for music discovery. Of course lots of things have happened in the year and a half since we put together that tutorial such as iPads, HTML5, plus tons more data availability. If you happen to have a favorite visualization for music discovery, post a link in the comments or send me an email: paul [at] echonest.com.
Whenever Jennie and I are in the car together, we will listen to the local Top-40 radio station (KISS 108). One top-40 artist that i can recognize reliably is Katy Perry. It seems like we can’t drive very far before we are listening to Teenage Dreams, Firework or California Gurls. That got me wondering what the average Time To Katy Perry (TTKP) was on the station and how it compared to other radio stations. So I fired up my Python interpreter, wrote some code to pull the data from the fabulous YES api and answer this very important question. With the YES API I can get the timestamped song plays for a station for the last 7 days. I gathered this data from WXKS (Kiss 108), did some calculations to come up with this data:
- Total songs played per week: 1,336
- Total unique songs: 184
- Total unique artists: 107
- Average songs per hour: 7
- Number of Katy Perry plays: 76
- Median Time between Katy Perry songs: 1hour 18 minutes
That means the average Time to Katy Perry is about 39 minutes.
Katy Perry is only the fourth most played artist on KISS 108. Here are the stats for the top 10:
|Artist||Plays|| Median time
| Average time
to next play
I took a look at some of the other top-40 stations around the country to see which has the lowest TTKP:
|Station||Songs Per Hour||TTKP|
|KIIS – LA’s #1 hit music station||8||39 mins|
|WHTZ- New York’s #1 hit music station||9||48 mins|
|WXKS- Boston’s #1 hit music station||7||39 mins|
|WSTR- Atlanta – Always #1 for Today’s Hit Music||8||38 mins|
|KAMP- 97.1 Amp Radio – Los Angeles||11||38 mins|
|KCHZ- 95.7 – The Beat of Kansas City||11||32 mins|
|WFLZ- 93.3 – Tampa Bay’s Hit Music channe||9||39 mins|
|KREV- 92.7 – The Revolution – San Francisco||11||36 mins|
So, no matter where you are, if you have a radio, you can tune into the local top-40 radio station, and you’ll need to wait, on average, only about 40 minutes until a Katy Perry song comes on. Good to know.