Supervised Single-Channel Speech Separation via Sparse Decomposition Using Periodic Signal Models
-
- NAKASHIZUKA Makoto
- Graduate School of Engineering Science, Osaka University
-
- OKUMURA Hiroyuki
- Graduate School of Engineering Science, Osaka University
-
- IIGUNI Youji
- Graduate School of Engineering Science, Osaka University
Search this article
Abstract
In this paper, we propose a method for supervised single-channel speech separation through sparse decomposition using periodic signal models. The proposed separation method employs sparse decomposition, which decomposes a signal into a set of periodic signals under a sparsity penalty. In order to achieve separation through sparse decomposition, the decomposed periodic signals have to be assigned to the corresponding sources. For the assignment of the periodic signal, we introduce clustering using a K-means algorithm to group the decomposed periodic signals into as many clusters as the number of speakers. After the clustering, each cluster is assigned to its corresponding speaker using preliminarily learnt codebooks. Through separation experiments, we compare our method with MaxVQ, which performs separation on the frequency spectrum domain. The experimental results in terms of signal-to-distortion ratio show that the proposed sparse decomposition method is comparable to the frequency domain approach and has less computational costs for assignment of speech components.
Journal
-
- IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
-
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E95.A (5), 853-866, 2012
The Institute of Electronics, Information and Communication Engineers
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390001206310070656
-
- NII Article ID
- 10030938110
- 130002133672
-
- NII Book ID
- AA10826239
-
- BIBCODE
- 2012IEITF..95..853N
-
- ISSN
- 17451337
- 09168508
-
- Text Lang
- en
-
- Data Source
-
- JaLC
- Crossref
- CiNii Articles
- KAKEN
-
- Abstract License Flag
- Disallowed