  Music source separation (MSS) aims to extract 'vocals', 'drums', 'bass' and
'other' tracks from a piece of mixed music. While deep learning methods have
shown impressive results, there is a trend toward larger models. In our paper,
we introduce a novel and lightweight architecture called DTTNet, which is based
on Dual-Path Module and Time-Frequency Convolutions Time-Distributed
Fully-connected UNet (TFC-TDF UNet). DTTNet achieves 10.12 dB cSDR on 'vocals'
compared to 10.01 dB reported for Bandsplit RNN (BSRNN) but with 86.7% fewer
parameters. We also assess pattern-specific performance and model
generalization for intricate audio patterns.
