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Monaural singing voice separation task focuses on the prediction of thesinging voice from a single channel music mixture signal. Current state of theart (SOTA) results in monaural singing voice separation are obtained with deeplearning based methods. In this work we present a novel deep learning basedmethod that learns long-term temporal patterns and structures of a musicalpiece. We build upon the recently proposed Masker-Denoiser (MaD) architectureand we enhance it with the Twin Networks, a technique to regularize a recurrentgenerative network using a backward running copy of the network. We evaluateour method using the Demixing Secret Dataset and we obtain an increment tosignal-to-distortion ratio (SDR) of 0.37 dB and to signal-to-interference ratio(SIR) of 0.23 dB, compared to previous SOTA results.

MaD TwinNet: Masker-Denoiser Architecture with Twin Networks for Monaural Sound Source Separation