Unsupervised analysis of polyphonic music by sparse coding

  title={Unsupervised analysis of polyphonic music by sparse coding},
  author={Samer A. Abdallah and Mark D. Plumbley},
  journal={IEEE Transactions on Neural Networks},
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… CONTINUE READING
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