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

October 29, 2018 · Entered Twilight · 🏛 Interspeech

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Repo contents: LICENSE, README.md, audio, config.md, config_multi_instrument.json, config_singing_voice.json, datasets.py, environment.yml, img, layers.py, main.py, models.py, separate.py, sessions, util.py

Authors Francesc Lluís, Jordi Pons, Xavier Serra arXiv ID 1810.12187 Category cs.SD: Sound Cross-listed cs.LG, eess.AS Citations 75 Venue Interspeech Repository https://github.com/francesclluis/source-separation-wavenet ⭐ 229 Last Checked 1 month ago

Abstract

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.