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arXiv · 1711.01437

Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask

Stylianos Ioannis Mimilakis·Konstantinos Drossos·João F. Santos·Gerald Schuller·Tuomas Virtanen·Yoshua Bengio
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Abstract

Singing voice separation based on deep learning relies on the usage of time-frequency masking. In many cases the masking process is not a learnable function or is not encapsulated into the deep learning optimization. Consequently, most of the existing methods rely on a post processing step using the generalized Wiener filtering. This work proposes a method that learns and optimizes (during training) a source-dependent mask and does not need the aforementioned post processing step. We introduce a recurrent inference algorithm, a sparse transformation step to improve the mask generation process, and a learned denoising filter. Obtained results show an increase of 0.49 dB for the signal to distortion ratio and 0.30 dB for the signal to interference ratio, compared to previous state-of-the-art approaches for monaural singing voice separation.

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Stylianos Ioannis Mimilakis, Konstantinos Drossos, João F. Santos, Gerald Schuller, Tuomas Virtanen, Yoshua Bengio. 2017-11-04. Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask. https://arxiv.org/abs/1711.01437

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