End-to-end music source separation: is it possible in the waveform domain?

Autor:	Lluís, Francesc, Pons, Jordi, Serra, Xavier
Rok vydání:	2018
Předmět:	Computer Science - Sound Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	Most of the currently successful source separation techniques use the magnitude spectrogram as input, and are therefore by default omitting part of the signal: the phase. To avoid omitting potentially useful information, we study the viability of using end-to-end models for music source separation --- which take into account all the information available in the raw audio signal, including the phase. Although during the last decades end-to-end music source separation has been considered almost unattainable, our results confirm that waveform-based models can perform similarly (if not better) than a spectrogram-based deep learning model. Namely: a Wavenet-based model we propose and Wave-U-Net can outperform DeepConvSep, a recent spectrogram-based deep learning model. Comment: In proceedings of INTERSPEECH 2019. Code: https://github.com/francesclluis/source-separation-wavenet and demo: http://jordipons.me/apps/end-to-end-music-source-separation/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1810.12187 Zobrazit plný text záznamu View this record from Arxiv