Music demixing with the sliCQ transform

TL;DR

This paper explores replacing the traditional STFT with the sliCQT transform in music demixing models, aiming to improve time-frequency resolution and separation quality, but finds that the new approach underperforms compared to existing methods.

Contribution

The paper introduces a novel adaptation of the UMX model using the sliCQT transform to address time-frequency resolution tradeoffs in music demixing.

Findings

xumx-sliCQ achieved lower scores than UMX

sliCQT offers varying resolution but did not improve separation

tradeoff in resolution impacts demixing performance

Abstract

Music source separation is the task of extracting an estimate of one or more isolated sources or instruments (for example, drums or vocals) from musical audio. The task of music demixing or unmixing considers the case where the musical audio is separated into an estimate of all of its constituent sources that can be summed back to the original mixture. The Music Demixing Challenge was created to inspire new demixing research. Open-Unmix (UMX), and the improved variant CrossNet-Open-Unmix (X-UMX), were included in the challenge as the baselines. Both models use the Short-Time Fourier Transform (STFT) as the representation of music signals. The time-frequency uncertainty principle states that the STFT of a signal cannot have maximal resolution in both time and frequency. The tradeoff in time-frequency resolution can significantly affect music demixing results. Our proposed adaptation of UMX replaced the STFT with the sliCQT, a time-frequency transform with varying time-frequency resolution. Unfortunately, our model xumx-sliCQ achieved lower demixing scores than UMX.