Low-Rank Sparse Representation with Pre-Learned Dictionaries and Side Information for Singing Voice Separation

Abstract

At present, although the human speech separation has achieved fruitful results, it is not ideal for the separation of singing and accompaniment. Based on low-rank and sparse optimization theory, in this paper, we propose a new singing voice separation algorithm called Low-rank, Sparse Representation with pre-learned dictionaries and side Information (LSRi). The algorithm incorporates both the vocal and instrumental spectrograms as sparse matrix and low-rank matrix, meanwhile combines pre-learning dictionary and the reconstructed voice spectrogram form the annotation. Evaluations on the iKala dataset show that the proposed methods are effective and efficient for singing voice separation.

Unsupervised Single-Channel Singing Voice Separation with Weighted Robust Principal Component Analysis Based on Gammatone Auditory Filterbank and Vocal Activity Detection

“…Yang [ 35 ] developed two novel improvements of the matrix decomposition of the magnitude spectrogram by fusing the harmonicity priors information. Later, by breaking down a mixted spectrogram into a multiple low-rank representation (MLRR) will be introduced [ 36 ]. Despite being effectively applied to SVS, RPCA fails when one singular value, such as drums, is significantly greater than others, which lowers the separation results, particularly for drums included in the combined music signal.…”