Po-Sen Huang, Scott Deeann Chen, Paris Smaragdis, Mark Hasegawa-Johnson, Prem Seetharaman (Implementation Author), Ethan Manilow (Implementation Author) University of Illinois, Northwestern University [email protected]
- is_blind: yes
- additional_training_data: no
- Code: http://github.com/my/repo
- Demos: http://www.demo.com
The nussl implementation of RPCA for audio source separation.
Abstract from the original paper:
Separating singing voices from music accompaniment is
an important task in many applications, such as music infor-
mation retrieval, lyric recognition and alignment. Music ac-
companiment can be assumed to be in a low-rank subspace,
because of its repetition structure; on the other hand, singing
voices can be regarded as relatively sparse within songs. In
this paper, based on this assumption, we propose using ro-
bust principal component analysis for singing-voice separa-
tion from music accompaniment. Moreover, we examine the
separation result by using a binary time-frequency masking
method. Evaluations on the MIR-1K dataset show that this
method can achieve around 1∼1.4 dB higher GNSDR com-
pared with two state-of-the-art approaches without using prior
training or requiring particular features.
- Huang, Po-Sen, et al. "Singing-voice separation from monaural recordings using robust principal component analysis." Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. IEEE, 2012.