  Singing voice separation attempts to separate the vocal and instrumental
parts of a music recording, which is a fundamental problem in music information
retrieval. Recent work on singing voice separation has shown that the low-rank
representation and informed separation approaches are both able to improve
separation quality. However, low-rank optimizations are computationally
inefficient due to the use of singular value decompositions. Therefore, in this
paper, we propose a new linear-time algorithm called informed group-sparse
representation, and use it to separate the vocals from music using pitch
annotations as side information. Experimental results on the iKala dataset
confirm the efficacy of our approach, suggesting that the music accompaniment
follows a group-sparse structure given a pre-trained instrumental dictionary.
We also show how our work can be easily extended to accommodate multiple
dictionaries using the DSD100 dataset.
