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Learning Disentangled Audio Representations through Controlled Synthesis

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arxiv 2402.10547 v1 pith:KM3A7YRO submitted 2024-02-16 cs.SD cs.LGeess.AS

Yusuf Brima , Ulf Krumnack , Simone Pika , Gunther Heidemann This is my paper
classification cs.SDcs.LGeess.AS
keywords audiobenchmarkingdisentangleddisentanglementlearningsyntoneauditorycontrolled
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This paper tackles the scarcity of benchmarking data in disentangled auditory representation learning. We introduce SynTone, a synthetic dataset with explicit ground truth explanatory factors for evaluating disentanglement techniques. Benchmarking state-of-the-art methods on SynTone highlights its utility for method evaluation. Our results underscore strengths and limitations in audio disentanglement, motivating future research.

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Cited by 1 Pith paper

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  1. Balancing Information Preservation and Disentanglement in Self-Supervised Music Representation Learning

    cs.SD 2025-07 conditional novelty 5.0 of 10

    A multi-view SSL framework with combined reconstruction and separation-based contrastive losses obtains disentangled pitch and instrument subspaces without the accuracy loss seen with contrastive-only training on NSynth.

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