Music Machinery

• Douglas Eck (Google)
• Greg Mead (Musicmetric)
• Martin Roth (RjDj)
• Ricardo Tarrasch (Meemix)
• moderator: Rebecca Fiebrink (Princeton)

• rjdj – music making apps on devices like iphones
• musicmetric tracks 3 areas: Social networks, network analysis (influential fans), text via focused crawlers, p2p networks
• memix – music recommendation, artist radio, artist similarity, playlists.  Pandora-like human analysis on 150K songs – then they learn these tags with machine learning.  Look at which features best predict the tags.  Important question is ‘what is important for the listeners’.  Their aim is to find best parameters for taste prediction.
• google – goal is organize the world’s information.   Doug would like to see an open API for companies to collaborate

Rebecca is the moderator.

What do you think is the next big thing? How is tech going to change things in the near future?

• Doug (Google) thinks that ‘music recommendation is solved’ – he’s excited about the cellphone.  Also excited about programs like chuck to make it easier for people to create music (nice pandering to the moderator, doug!)
• Ricardo  (MeeMix) – the laid back position is the future – reach the specific taste of a user.  Personalized advertisements.
• Greg (MusicMetric) – Cloudbased services will help us understand what people want which will yield to playlisting, recommendation, novel players.
• Martin (RjDJ) – Thinks that the phone is really exciting – having all this power in the phone lets you do neat thing.  He’s excited about how people will be able to create music – using sensory inputs, ambient audio.

How will tech revolutionize music?

• Doug – being able to collaborate with Arcade Fire on online
• Martin – musically illiterate should be able to make music
• Ricardo – we can help new artists reach the right fans
• Greg – services for helping artists, merchandising, ticket sales etc.

What are the most interesting problems or technical questions?

• Greg – interested in understanding the behavior of the fans. Especially by those on P2P networks. Huge amount of geographic-specific listener data
• Ricardo – more research around taste and recommendation
• Doug – a rant – he had a paper rejected because the paper had something to do with music generation.
• Rebecca – has a MIR for music google group :MIR4Music
• Martin – engineering:increase performance in portable devices – research:how to extract music features from music cheaply
• Ricardo – drumming style is hard to extract – but actually not that important for taste prediction

How would you characterize the relationship between biz and academia

• Greg – there is lots of  ‘advanced research’ in academia, while in industry  there look at much more applied problems
• Doug – suggests that the leader of an academic lab is key to bridging the gap between biz and academia.  Grad students should be active in looking for the internships in industry to get a better understanding of what is needed in industry.  It is all about getting grad students jobs in industry.

Audience Q/A

• what tools can we create to help producers of music? – Answer: Youtube. Martin talks about understanding how people use music creation tools.   Doug: “Don’t build things that people don’t want.”  – to do this you need to try this on real data.

Hmmm … only one audience q/a.  sigh …

Good panel, lots of interesting ideas.  Here is the future of music: