Unsupervised Learning Methods for Source Separation in Monaural Music Signals

Abstract

Computational analysis of polyphonic musical audio is a challenging problem. When several instruments are played simultaneously, their acoustic signals mix, and estimation of an individual instrument is disturbed by the other cooccurring sounds. The analysis task would become much easier if there was a way to separate the signals of different instruments from each other. Techniques that implement this are said to perform sound source separation. The separation would not be needed if a multi-track studio recording was available where the signal of each instrument is on its own channel. Also, recordings done with microphone arrays would allow more efficient separation based on the spatial location of each source. However, multi-channel recordings are usually not available; rather, music is distributed in stereo format. This chapter discusses sound source separation in monaural music signals, a term which refers to a one-channel signal obtained by recording with a single microphone or by mixing down several channels.