  In recent years, deep neural networks (DNNs) based approaches have achieved
the start-of-the-art performance for music source separation (MSS). Although
previous methods have addressed the large receptive field modeling using
various methods, the temporal and frequency correlations of the music
spectrogram with repeated patterns have not been explicitly explored for the
MSS task. In this paper, a temporal-frequency attention module is proposed to
model the spectrogram correlations along both temporal and frequency
dimensions. Moreover, a multi-scale attention is proposed to effectively
capture the correlations for music signal. The experimental results on MUSDB18
dataset show that the proposed method outperforms the existing state-of-the-art
systems with 9.51 dB signal-to-distortion ratio (SDR) on separating the vocal
stems, which is the primary practical application of MSS.
