On the Improvement of Singing Voice Separation for Monaural Recordings Using the MIR-1K Dataset

Abstract

Monaural singing voice separation is an extremely challenging problem. While efforts in pitch-based inference methods have led to considerable progress in voiced singing voice separation, little attention has been paid to the incapability of such methods to separate unvoiced singing voice due to its in harmonic structure and weaker energy. In this paper, we proposed a systematic approach to identify and separate the unvoiced singing voice from the music accompaniment. We have also enhanced the performance of separating voiced singing via a spectral subtraction method. The proposed system follows the framework of computational auditory scene analysis (CASA) which consists of the segmentation stage and the grouping stage. In the segmentation stage, the input song signals are decomposed into small sensory elements in different time-frequency resolutions. The unvoiced sensory elements are then identified by Gaussian mixture models. The experimental results demonstrated that the quality of the separated singing voice is improved for both the unvoiced and voiced parts. Moreover, to deal with the problem of lack of a publicly available dataset for singing voice separation, we have constructed a corpus called MIR-1K (multimedia information retrieval lab, 1000 song clips) where all singing voices and music accompaniments were recorded separately. Each song clip comes with human-labeled pitch values, unvoiced sounds and vocal/non-vocal segments, and lyrics, as well as the speech recording of the lyrics.