Vocal Separation from Monaural Music Using Temporal/Spectral Continuity and Sparsity Constraints

In this letter, we describe a novel approach for separating a vocal signal from monaural music. We assume that the accompaniment in a music signal can be represented as the sum of the sustained harmonic and percussive sounds. Based on the observation that singing voices usually contain rapidly changing harmonic signals such as fast vibratos, slides, and/or glissandos, we propose a statistical model for the separation of harmonic/percussive and vocal sounds. To this end, we define an objective function that exploits the temporal/spectral continuities of harmonic/percussive sounds and the sparsity of vocal sounds in the spectrogram domain. Experimental results show that the proposed algorithm successfully separates the vocal from the accompaniment, resulting in a performance significantly better than that of conventional algorithms or comparable to the state-of-the-art algorithms.