Stereophonic Music Source Separation with Spatially-Informed Bridging Band-Split Network

Abstract

Stereophonic music source separation (MSS) is a problem of extracting individual source tracks, e.g. bass, drums, vocals, from a stereo music recording. Deep neural network (DNN) based MSS systems have demonstrated great promise though spatial panning cues and time-frequency spectral structures in stereo music have not yet been fully explored in such systems and methods. This paper presents a spatially-informed MSS method using a bridging band-split neural network that incorporates both spatial and spectral information. The spatial panning angles of each target source are used as input of the network, along with the time-frequency spectrograms. Moreover, the inter-track correlations are exploited for further performance improvement. Experiments show that the proposed method outperforms significantly the baseline systems as the result of using spatial cues, spectral characteristics, and inter-track relationships.