Learning Disentangled Audio Representations through Controlled Synthesis
<|reference_start|>Learning Disentangled Audio Representations through Controlled Synthesis: 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.<|reference_end|>
@article{brima2024learning,
    title={Learning Disentangled Audio Representations through Controlled Synthesis},
    author={Yusuf Brima, Ulf Krumnack, Simone Pika and Gunther Heidemann},
    journal={arXiv preprint arXiv:2402.10547},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10547},
    primaryClass={cs.SD cs.LG eess.AS}
}