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.
|Title of host publication||Proceedings of 2021 IEEE International Conference on Acoustics, Speech and Signal Processing|
|Publication status||Published - 2021|
|Event||2021 IEEE International Conference on Acoustics, Speech and Signal Processing - Metro Toronto Convention Centre, Toronto, Canada|
Duration: 6 Jun 2021 → 11 Jun 2021
|Conference||2021 IEEE International Conference on Acoustics, Speech and Signal Processing|
|Location||Metro Toronto Convention Centre|
|Period||06/06/2021 → 11/06/2021|