I am at ISMIR this week, blogging sessions and papers that I find interesting.
Identifying Repeated Patterns in Music using Sparse Convolutive Non-Negative Matrix Factorization – Ron Weiss, Juan Bello (pdf)
Problem: Looking at repetition in music – verse, chorus, repeated motifs. Can one identify high level and short term structiure simulataneous from audio? Lots of math in this.
Ron describes an unsupervised, data-driven, method for automatically identifying repeated patterns in music by analyzing a feature matrix using a variant of sparse convolutive non-negative matrix factorization. They utilize sparsity constraints to automatically identify the number of patterns and their lengths, parameters that would normally need to be fixed in advance. The proposed analysis is applied to beat- synchronous chromagrams in order to concurrently extract repeated harmonic motifs and their locations within a song. They show how this analysis can be used for long- term structure segmentation, resulting in an algorithm that is competitive with other state-of-the-art segmentation algorithms based on hidden Markov models and self similarity matrices.
One particular application is riff identification for music thumbnailing. Another application is structure segmentation – verse chorus, bridge etc.)
The code is open-sourced here: http://ronw.github.com/siplca-segmentation/
This was a really interesting presentation, with great examples. Excellent work. This one should be a candidate for best paper IMHO.