Learning Disentangled Audio Representations through Controlled Synthesis
CoRR(2024)
摘要
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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