Separate This, and All of these Things Around It: Music Source Separation via Hyperellipsoidal Queries

TL;DR

This paper introduces a novel music source separation method using hyperellipsoidal queries that allows flexible, query-based extraction of musical components, achieving state-of-the-art results on the MoisesDB dataset.

Contribution

It proposes a new query-by-region system with hyperellipsoidal regions for flexible source separation, moving beyond fixed-stem paradigms.

Findings

Achieved state-of-the-art signal-to-noise ratios.

Demonstrated effective retrieval metrics.

Flexible query specification improves separation performance.

Abstract

Music source separation is an audio-to-audio retrieval task of extracting one or more constituent components, or composites thereof, from a musical audio mixture. Each of these constituent components is often referred to as a "stem" in literature. Historically, music source separation has been dominated by a stem-based paradigm, leading to most state-of-the-art systems being either a collection of single-stem extraction models, or a tightly coupled system with a fixed, difficult-to-modify, set of supported stems. Combined with the limited data availability, advances in music source separation have thus been mostly limited to the "VDBO" set of stems: \textit{vocals}, \textit{drum}, \textit{bass}, and the catch-all \textit{others}. Recent work in music source separation has begun to challenge the fixed-stem paradigm, moving towards models able to extract any musical sound as long as this target type of sound could be specified to the model as an additional query input. We generalize this idea to a \textit{query-by-region} source separation system, specifying the target based on the query regardless of how many sound sources or which sound classes are contained within it. To do so, we propose the use of hyperellipsoidal regions as queries to allow for an intuitive yet easily parametrizable approach to specifying both the target (location) as well as its spread. Evaluation of the proposed system on the MoisesDB dataset demonstrated state-of-the-art performance of the proposed system both in terms of signal-to-noise ratios and retrieval metrics.