Stripe-Transformer: deep stripe feature learning for music source separation

Abstract

Music source separation (MSS) is to isolate musical instrument signals from the given music mixture. Stripes widely exist in music spectrograms, which potentially indicate high-level music information. For example, a vertical stripe indicates a drum time and a horizontal stripe indicates a harmonic component such as a singing voice. These stripe features actually affect the performance of MSS systems, which has not been explicitly explored by previous MSS studies. In this paper, we propose stripe-Transformer, a deep stripe feature learning method for MSS with a Transformer-based architecture. Stripe-wise self-attention mechanism is designed to capture global dependencies along the time and frequency axis in music spectrograms. Experimental results on the Musdb18 dataset show that our proposed model reaches an average source-to-distortion (SDR) of 6.71dB on four target sources, achieving state-of-the-art performance with fewer parameters. And the visualization results show the capability of the proposed model to extract beat and harmonic structure in music signals.