Informed monaural source separation of music based on convolutional sparse coding
Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Journal Volume
2015-August
ISBN
9781467369978
Date Issued
2015-08-04
Author(s)
Abstract
Monaural source separation is a challenging problem that has many important applications in music information retrieval. In this paper, we focus on the score-informed variant of this problem. While non-negative matrix factorization and some other approaches have been shown effective, few existing approaches have properly taken the phase information into account. There are unnatural sound in the separation result, as the phase of each source signal is considered equivalent to the phase of the mixed signal. To remedy this, we propose to perform source separation directly in the time domain using a convolutional sparse coding (CSC) approach. Evaluation on the Bach10 dataset shows that, when the instrument, pitch and onset/offset time are informed, the source to distortion ratio of the separation result reaches 8.59 dB, which is 2.02 dB higher than a state-of-the-art system called Soundprism.
Subjects
Convolutional sparse coding | dictionary learning | score-informed monaural source separation
Type
conference paper