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arXiv · 2402.10547

Learning Disentangled Audio Representations through Controlled Synthesis

Yusuf Brima·Ulf Krumnack·Simone Pika·Gunther Heidemann
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Abstract

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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Yusuf Brima, Ulf Krumnack, Simone Pika, Gunther Heidemann. 2024-02-16. Learning Disentangled Audio Representations through Controlled Synthesis. https://arxiv.org/abs/2402.10547

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