| Paper ID | AUD-29.2 |
| Paper Title |
DEFICIENT BASIS ESTIMATION OF NOISE SPATIAL COVARIANCE MATRIX FOR RANK-CONSTRAINED SPATIAL COVARIANCE MATRIX ESTIMATION METHOD IN BLIND SPEECH EXTRACTION |
| Authors |
Yuto Kondo, Yuki Kubo, Norihiro Takamune, University of Tokyo, Japan; Daichi Kitamura, National Institute of Technology, Kagawa College, Japan; Hiroshi Saruwatari, University of Tokyo, Japan |
| Session | AUD-29: Acoustic Sensor Array Processing 3: Acoustic Sensor Arrays |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-SEP] Audio and Speech Source Separation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Rank-constrained spatial covariance matrix estimation (RCSCME) is a state-of-the-art blind speech extraction method applied to cases where one directional target speech and diffuse noise are mixed. In this paper, we proposed a new algorithmic extension of RCSCME. RCSCME complements a deficient one rank of the diffuse noise spatial covariance matrix, which cannot be estimated via preprocessing such as independent low-rank matrix analysis, and estimates the source model parameters simultaneously. In the conventional RCSCME, a direction of the deficient basis is fixed in advance and only the scale is estimated; however, the candidate of this deficient basis is not unique in general. In the proposed RCSCM model, the deficient basis itself can be accurately estimated as a vector variable by solving a vector optimization problem. Also, we derive new update rules based on the EM algorithm. We confirm that the proposed method outperforms conventional methods under several noise conditions. |
