Posts Tagged sxsw
I recently gave a talk on Data Mining Music at SXSW. It was a standing room only session, with an enthusiastic audience that asked great questions. It was a really fun time for me. I’ve posted the slides to Slideshare, but be warned that there are no speaker notes so it may not always be clear what any particular slide is about. There was lots of music in the talk, but unfortunately, it is not in the Slideshare PDF. The links below should flesh out most of the details and have some audio examples.
- Have artist names been getting longer?
- The Passion Index – Find the bands that have the most passionate fans
- Six Degrees of Black Sabbath – Using artist relationship data to build a Six Degrees of Kevin Bacon for Music
- Frog-based playlisting – Building advanced playlists by finding paths through the artist space
- The Click Track Detector – Finding drummers that use a click track
- Looking for the Slow Build – Finding songs that have a gradual build
- Bohemian Rhapsichord – Turning a popular song into a musical instrument, with data.
- Midem Music Machine – Making a beautiful visualization of music
- The Swinger – Making any song swing
Thanks to everyone who attended.
If you happen to be in Austin this week for SXSW consider attending my talk called Data Mining Music. It is all about the fun things you can discover about music when you have data about millions of songs and artists.
The talk is on Sunday, Marcy 11 at 5:00PM in the Rio Grande room of the Hilton Garden Inn. All the details are here: Data Mining Music
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 finished giving my talk at SXSW called – ‘Finding Music with Pictures”. A few people asked for the slides – I’ve posted them to Slideshare. Of course all the audio and video is gone, but you can follow the links to see the vids. Here are the slides:
Looking for a tool to help you find the best bands to see in Austin during SXSW? Check out the Festival Explorer – Austin Edition:
It uses Echo Nest data like hotttness, top terms, similar artists to give you all sorts of ways to explore the over 2,000 artists playing in Austin during the next week. The Festival Explorer is a free iPhone app, available in the app store now: Festival Explorer Austin Edition
There are thousands of artists playing at SXSW this year. To help sort it all out, I thought I’d adapt my Music Maze to work within the world of SXSW 2011 artists. It is a good way to figure out which bands you’d like to see.
This visualization fits in with the SXSW talk I’m giving in a few days: Finding Music With Pictures