Informed monaural source separation of music based on convolutional sparse coding

• Published in: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 236 - 240
• Main Authors: Ping-Keng Jao, Yi-Hsuan Yang, Wohlberg, Brendt
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2015
• Subjects: Convolution; Convolutional codes; Convolutional sparse coding; Dictionaries; dictionary learning; Instruments; score-informed monaural source separation; Source separation; Speech; Time-domain analysis
• ISSN: 1520-6149; 2379-190X
• DOI: 10.1109/ICASSP.2015.7177967

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.