| Paper: | SS-6.2 | ||
| Session: | Convolutive Blind Source Separation for Speech and Audio Signals | ||
| Time: | Wednesday, May 19, 15:50 - 16:10 | ||
| Presentation: | Special Session Lecture | ||
| Topic: | Special Sessions: Convolutive Blind Source Separation for Speech and Audio Signals | ||
| Title: | UNDERDETERMINED BLIND SEPARATION FOR SPEECH IN REAL ENVIRONMENTS WITH SPARSENESS AND ICA | ||
| Authors: | Shoko Araki; NTT Corporation | ||
| Shoji Makino; NTT Corporation | |||
| Audrey Blin; Université du Québec | |||
| Ryo Mukai; NTT Corporation | |||
| Hiroshi Sawada; NTT Corporation | |||
| Abstract: | In this paper, we propose a method for separating speech signals when there are more signals than sensors. Several methods have already been proposed for solving the underdetermined problem, and some of these utilize the sparseness of speech signals. These methods employ binary masks to extract the signals, and therefore, their extracted signals contain loud musical noise. To overcome this problem, we propose combining a sparseness approach and independent component analysis (ICA). First, using sparseness, we estimate the time points when only one source is active. Then, we remove this single source from the observations and apply ICA to the remaining mixtures. Experimental results show that our proposed sparseness and ICA (SPICA) method can separate signals with little distortion even in reverberant conditions of T_R=130 and 200 ms. | ||
| Back | |||
Home -||-
Organizing Committee -||-
Technical Committee -||-
Technical Program -||-
Plenaries
Paper Submission -||-
Special Sessions -||-
ITT -||-
Paper Review -||-
Exhibits -||-
Tutorials
Information -||-
Registration -||-
Travel Insurance -||-
Housing -||-
Workshops