Exécution de notre outil de synthèse sur un nouvel article
Vérifiez l’article ci-dessous, puis lancez sa synthèse.
Soft Disentanglement in Frequency Bands for Neural Audio Codecs
Benoit Ginies (Institut Polytechnique de Paris, Laboratoire Traitement et Communication de l’Information), Xiaoyu Bie (Institut Polytechnique de Paris, Laboratoire Traitement et Communication de l’Information), Olivier Fercoq (Institut Polytechnique de Paris, Laboratoire Traitement et Communication de l’Information), Gaël Richard A (Institut Polytechnique de Paris, Laboratoire Traitement et Communication de l’Information)
Résumé : In neural-based audio feature extraction, ensuring that representations capture disentangled information is crucial for model interpretability. However, existing disentanglement methods often rely on assumptions that are highly dependent on data characteristics or specific tasks. In this work, we introduce a generalizable approach for learning disentangled features within a neural architecture. Our method applies spectral decomposition to time-domain signals, followed by a multibranch audio codec that operates on the decomposed components. Empirical evaluations demonstrate that our approach achieves better reconstruction and perceptual performance compared to a state-of-the-art baseline while also offering potential advantages for inpainting tasks.