Unsupervised analysis of polyphonic music by sparse coding

IEEE Transactions on Neural Networks
Samer A Abdallah, Mark D Plumbley

Abstract

We investigate a data-driven approach to the analysis and transcription of polyphonic music, using a probabilistic model which is able to find sparse linear decompositions of a sequence of short-term Fourier spectra. The resulting system represents each input spectrum as a weighted sum of a small number of "atomic" spectra chosen from a larger dictionary; this dictionary is, in turn, learned from the data in such a way as to represent the given training set in an (information theoretically) efficient way. When exposed to examples of polyphonic music, most of the dictionary elements take on the spectral characteristics of individual notes in the music, so that the sparse decomposition can be used to identify the notes in a polyphonic mixture. Our approach differs from other methods of polyphonic analysis based on spectral decomposition by combining all of the following: (a) a formulation in terms of an explicitly given probabilistic model, in which the process estimating which notes are present corresponds naturally with the inference of latent variables in the model; (b) a particularly simple generative model, motivated by very general considerations about efficient coding, that makes very few assumptions about the musical orig...Continue Reading

References

Apr 2, 1998·Proceedings. Biological Sciences·J H van Hateren, A van der Schaaf
Jan 15, 2000·Neural Computation·M S Lewicki, T J Sejnowski
Sep 21, 2001·Network : Computation in Neural Systems·H Barlow
Mar 16, 2002·Nature Neuroscience·Michael S Lewicki
May 15, 2003·Current Opinion in Neurobiology·Eero P Simoncelli
May 1, 1954·Psychological Review·F ATTNEAVE
Sep 24, 2004·IEEE Transactions on Neural Networks·Liqing ZhangShun-ichi Amari
Feb 2, 2008·IEEE Transactions on Neural Networks·M D Plumbley

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Citations

Nov 12, 2013·Neural Computation·Ashkan Amiri, Simon Haykin
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Xiaoqiang LuYuan Yuan

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