Automatic gathering strategy for unsupervised source separation algorithms

Abstract

Unsupervised learning algorithms for audio source separation such as non-negative matrix factorization (NMF) and principal components analysis (PCA) can be understood as a data matrix factorization subject to different constraints. These algorithms provide components with a relevant structure and homogeneous musical events. The invention presents an automatic fusion method to merge these components into tracks associated to the different instruments present in the sound source.

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