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?
Music Machinery 
#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!