ISMIR 2010: recommendation

Brian McFee presented a poster on Learning Similarity from Collaborative Filters describing a method of addressing the cold start problem. The idea is to learn to rank tag representations of artists or tracks, so that the ranks agree with similarity scores given by collaborative filtering, using Metric Learning to Rank, an extension of the ranking SVM. This shows a modest improvement when applied to autotags inferred from audio, but really good results when applied to musicological tags scraped from Pandora. Conclusion: if you have musicological tags then you may not need CF data to get really good artist similarities.

Although at the moment musicological tags are probably even harder to come by than CF data, there are encouraging signs of progress in autotagging. A nice paper by James Bergstra, Mike Mandel and Doug Eck on Scalable Genre and Tag Prediction with Spectral Covariance shows impressive results with a scalable framework that learns from simple spectral features.

Along with several submissions to the MIREX evaluation tasks, this paper also illustrates another small trend at ISMIR 2010, which is a move away from known flawed features, in this case MFCCs used to represent musical timbre.