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
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?