  Harmonic/percussive source separation (HPSS) consists in separating the
pitched instruments from the percussive parts in a music mixture. In this
paper, we propose to apply the recently introduced Masker-Denoiser with twin
networks (MaD TwinNet) system to this task. MaD TwinNet is a deep learning
architecture that has reached state-of-the-art results in monaural singing
voice separation. Herein, we propose to apply it to HPSS by using it to
estimate the magnitude spectrogram of the percussive source. Then, we retrieve
the complex-valued short-time Fourier transform of the sources by means of a
phase recovery algorithm, which minimizes the reconstruction error and enforces
the phase of the harmonic part to follow a sinusoidal phase model. Experiments
conducted on realistic music mixtures show that this novel separation system
outperforms the previous state-of-the art kernel additive model approach.
