Binaural Segregation in Multisource Reverberant Environments
Abstract
In a natural environment, speech signals are degraded by both reverberation and concurrent noise sources. While human listening is robust under these conditions using only two ears, current two-microphone algorithms perform poorly. The psychological process of figure ground segregation suggests that the target signal is perceived as foreground while the remaining stimuli are perceived as background. Accordingly, our goal is to estimate an ideal time-frequency (T-F) binary mask, which selects the target if it is stronger than the interference in a local T-F unit. In this paper, we propose a binaural segregation system which extracts the reverberant target signal from multisource reverberant mixtures by utilizing only the location information of target source. The proposed system combines target cancellation through adaptive filtering and a binary decision rule to estimate the ideal T-F binary mask. A key observation in this work is that the attenuation due to target cancellation in a T-F unit is systematically correlated with the relative strength between target and interference. A comprehensive evaluation shows that the proposed system results in large SNR gains. In addition, comparisons using SNR as well as automatic speech recognition measures show that our system outperforms standard two-microphone beamforming approaches and a recent binaural processor.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2005
- Accession Number
- AD1001209
Entities
People
- DeLiang Wang
- Nicoleta Roman
- Soundararajan Srinivasan
Organizations
- Ohio State University