  This paper presents a new input format, channel-wise subband input (CWS), for
convolutional neural networks (CNN) based music source separation (MSS) models
in the frequency domain. We aim to address the major issues in CNN-based
high-resolution MSS model: high computational cost and weight sharing between
distinctly different bands. Specifically, in this paper, we decompose the input
mixture spectra into several bands and concatenate them channel-wise as the
model input. The proposed approach enables effective weight sharing in each
subband and introduces more flexibility between channels. For comparison
purposes, we perform voice and accompaniment separation (VAS) on models with
different scales, architectures, and CWS settings. Experiments show that the
CWS input is beneficial in many aspects. We evaluate our method on musdb18hq
test set, focusing on SDR, SIR and SAR metrics. Among all our experiments, CWS
enables models to obtain 6.9% performance gain on the average metrics. With
even a smaller number of parameters, less training data, and shorter training
time, our MDenseNet with 8-bands CWS input still surpasses the original
MMDenseNet with a large margin. Moreover, CWS also reduces computational cost
and training time to a large extent.
