| Paper ID | AUD-8.5 |
| Paper Title |
EXPLOITING NON-NEGATIVE MATRIX FACTORIZATION FOR BINAURAL SOUND LOCALIZATION IN THE PRESENCE OF DIRECTIONAL INTERFERENCE |
| Authors |
Ingvi Örnolfsson, Torsten Dau, Technical University of Denmark, Denmark; Ning Ma, University of Sheffield, United Kingdom; Tobias May, Technical University of Denmark, Denmark |
| Session | AUD-8: Audio and Speech Source Separation 4: Multi-Channel Source Separation |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 |
| 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 |
This study presents a novel solution to the problem of binaural localization of a speaker in the presence of interfering directional noise and reverberation. Using a state-of-the-art binaural localization algorithm based on a deep neural network (DNN), we propose adding a source separation stage based on non-negative matrix factorization (NMF) to improve the localization performance in conditions with interfering sources. The separation stage is coupled with the localization stage, and is optimized with respect to a broad range of different acoustic conditions, emphasizing a robust and generalizable solution. The machine listening system is shown to greatly benefit from the NMF-based separation stage at low target-to-masker ratios (TMRs) for a variety of noise types, especially for non-stationary noise. It is also demonstrated that training the NMF algorithm on anechoic speech provides better performance than using reverberant speech, and that optimizing the source separation stage using a localization metric rather than a source separation metric substantially increases the system performance. |
