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

• URL: http://arxiv.org/abs/2501.16171v1
• Date: Mon, 27 Jan 2025 16:13:50 GMT
• Title: Separate This, and All of these Things Around It: Music Source Separation via Hyperellipsoidal Queries
• Authors: Karn N. Watcharasupat, Alexander Lerch,
• Abstract summary: Music source separation is an audio-to-audio retrieval task. Recent work in music source separation has begun to challenge the fixed-stem paradigm. We propose the use of hyperellipsoidal regions as queries to allow for an intuitive yet easily parametrizable approach to specifying both the target (location) and its spread.
• Score: 53.30852012059025
• License:
• 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.

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