Harmonic-aware tri-path convolution recurrent network for singing voice separation

Abstract

Temporal coherence and spectral regularity are critical cues for human auditory streaming processes and are considered in many sound separation models. Some examples include the Conv-tasnet model, which focuses on temporal coherence using short length kernels to analyze sound, and the dual-path convolution recurrent network (DPCRN) model, which uses two recurring neural networks to analyze general patterns along the temporal and spectral dimensions on a spectrogram. By expanding DPCRN, a harmonic-aware tri-path convolution recurrent network model via the addition of an inter-band RNN is proposed. Evaluation results on public datasets show that this addition can further boost the separation performances of DPCRN.