TY - GEN
T1 - Spatial feature learning for robust binaural sound source localization using a composite feature vector
AU - Wu, Xiang
AU - Talagala, Dumidu S.
AU - Zhang, Wen
AU - Abhayapala, Thushara D.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/5/18
Y1 - 2016/5/18
N2 - The performance of binaural speech source localization systems can be significantly impacted by an imperfect selection of spatial localization cues, due to the limited bandwidth of speech, and the effects of noise. In order to mitigate these impacts, this paper presents a novel method that combines a deterministic localization approach with a spatial feature learning process. Here, we (i) obtain a composite feature vector derived from analysing the mutual information between different spatial cues and (ii) estimate the optimum feature combination that minimizes the angular localization error in three dimensional space. The performance of the proposed mutual information based feature learning approach is evaluated for a range of speech inputs and noise conditions. We also demonstrate that the proposed approach improves the localization accuracy and its robustness, with respect to traditional localization algorithms, especially in the relatively low signal-to-noise ratio localization scenarios.
AB - The performance of binaural speech source localization systems can be significantly impacted by an imperfect selection of spatial localization cues, due to the limited bandwidth of speech, and the effects of noise. In order to mitigate these impacts, this paper presents a novel method that combines a deterministic localization approach with a spatial feature learning process. Here, we (i) obtain a composite feature vector derived from analysing the mutual information between different spatial cues and (ii) estimate the optimum feature combination that minimizes the angular localization error in three dimensional space. The performance of the proposed mutual information based feature learning approach is evaluated for a range of speech inputs and noise conditions. We also demonstrate that the proposed approach improves the localization accuracy and its robustness, with respect to traditional localization algorithms, especially in the relatively low signal-to-noise ratio localization scenarios.
KW - Binaural localization
KW - generalized cross correlation
KW - head related transfer function (HRTF)
KW - mutual information
UR - https://www.scopus.com/pages/publications/84973320225
U2 - 10.1109/ICASSP.2016.7472893
DO - 10.1109/ICASSP.2016.7472893
M3 - 会议稿件
AN - SCOPUS:84973320225
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 6320
EP - 6324
BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Y2 - 20 March 2016 through 25 March 2016
ER -