Name
Abstract | ||
|---|---|---|
Sound source localization from a binaural input is a challenging problem, particularly when multiple sources are active simultaneously and reverberation or background noise are present. In this work, we investigate a multi-source localization framework in which monaural source segregation is used as a mechanism to increase the robustness of azimuth estimates from a binaural input. We demonstrate performance improvement relative to binaural only methods assuming a known number of spatially stationary sources. We also propose a flexible azimuth-dependent model of binaural features that independently captures characteristics of the binaural setup and environmental conditions, allowing for adaptation to new environments or calibration to an unseen binaural setup. Results with both simulated and recorded impulse responses show that robust performance can be achieved with limited prior training. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TASL.2012.2183869 | IEEE Transactions on Audio, Speech & Language Processing |
Keywords | Field | DocType |
sound source localization,estimation,reverberation,sound localization,time frequency analysis,computational auditory scene analysis,feature extraction,azimuth,impulse response,noise measurement,audio signal processing | Reverberation,Background noise,Noise measurement,Computer science,Robustness (computer science),Speech recognition,Audio signal processing,Binaural recording,Monaural,Acoustic source localization | Journal |
Volume | Issue | ISSN |
20 | 5 | 1558-7916 |
Citations | PageRank | References |
46 | 1.82 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Woodruff | 1 | 148 | 12.17 |
| DeLiang Wang | 2 | 49 | 2.71 |