Learning Disentangled Audio Representations through Controlled Synthesis
Yusuf Brima Ulf Krumnack Simone Pika Gunther Heidemann
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.