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

Chenghong Yang,Hongjuan Zhang

doi:10.4236/apm.2018.84024

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.

Highlights

Separating singing voice from music recording is very useful in many applications, such as music information retrieval, singer identification and lyrics recognition and alignment [1]
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)
Robust Principal Component Analysis (RPCA) unsupervised method proposed by Huang et al [3], use default parameter values λ =

Summary

Introduction

Separating singing voice from music recording is very useful in many applications, such as music information retrieval, singer identification and lyrics recognition and alignment [1]. The human auditory system can distinguish the vocal and instrumental of music recording, it is extremely difficult for computer systems. In this context, researchers are increasingly concerned with the mining of music information. Many algorithms have been proposed to separate singing voice from music recording. Robust Principal Component Analysis (RPCA) is a matrix factorization algorithm for solving underlying low-rank and sparse matrices [2].

Methods

Results

Conclusion