MIR at Google: Strategies for Scaling to Large Music Datasets Using Ranking and Auditory Sparse-Code Representations

MIR at Google: Strategies for Scaling to Large Music Datasets Using Ranking and Auditory Sparse-Code Representations
Douglas Eck (Google) (Invited speaker) – There’s no paper associated with this talk.

Machine Listening / Audio analysis – Dick Lyon and Samy Bengio

Main strength:

  • Scalable algorithms
    • When they do work, they use large sets (like all audio on Youtube, or all audio on the web)
  • Sparse High dimensional Representations
    • 15 numbers to describe a track
  • Auditory / Cohchlear Modeling
  • Autotagging at Youtube -
  • Retrieval, annotation, ranking, recommendation

Collaboration Opportunities

  • Faculty research awards
  • Google visiting faculty program
  • Student internships
  • Google summer of code
  • Research Infrastructure

The Future of MIR is already here

  • Next generation of listeners are using Youtube – because of the on-demand nature
  • Youtube – 2 billion views a day
  • Content ID scans over 100 years of video every day

The Bar is already set very high ..

  • Current online recommendation is pretty good
  • Doug wants to close the loop between music making and music listening

What would you like Google to give back to MIR?

  1. #1 by Itman on August 13, 2010 - 12:56 pm

    This is very interesting. Do they rely more on user-generated meta-data or do they focus on content-based tagging and retrieval!
    Thank you!

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