Binaural Target Sound Source Localization Based on Time-frequency Units Selection

Ruwei LI; Tao LI; Xiaoyue SUN; Dengcai YANG; Qi WANG

doi:10.11999/JEIT181127

Volume 41 Issue 12

Dec. 2019

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(12): 2932-2938

Ruwei LI, Tao LI, Xiaoyue SUN, Dengcai YANG, Qi WANG. Binaural Target Sound Source Localization Based on Time-frequency Units Selection[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2932-2938. doi: 10.11999/JEIT181127

Citation:

Ruwei LI, Tao LI, Xiaoyue SUN, Dengcai YANG, Qi WANG. Binaural Target Sound Source Localization Based on Time-frequency Units Selection[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2932-2938. doi: 10.11999/JEIT181127

Citation:

PDF( 561 KB)

Binaural Target Sound Source Localization Based on Time-frequency Units Selection

doi: 10.11999/JEIT181127

1.
Laboratory of Speech and Audio Signal Processing and Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2.
Institute of Science and Technology Development, Beijing University of Technology, Beijing 100124, China

Funds: The National Natural Science Foundation of China(51477028), The Scientific Research Program of Beijing Municipal Commission of Education (KM201510005007)

Received Date: 2018-12-06
Rev Recd Date: 2019-05-21

Available Online: 2019-06-04

Publish Date: 2019-12-01

Abstract

Abstract

The performance of the existing target localization algorithms is not ideal in complex acoustic environment. In order to improve this problem, a novel target binaural sound localization algorithm is presented. First, the algorithm uses binaural spectral features as input of a time-frequency units selector based on deep learning. Then, to reduce the negative impact of the time-frequency unit belonging to noise on the localization accuracy, the selector is emploied to select the reliable time-frequency units from binaural input sound signal. At the same time, a Deep Neural Network (DNN)-based localization system maps the binaural cues of each time-frequency unit to the azimuth posterior probability. Finally, the target localization is completed according to the azimuth posterior probability belonging to the reliable time-frequency units. Experimental results show that the performance of the proposed algorithm is better than comparison algorithms and achieves a significant improvement in target localization accuracy in low Signal-to-Noise Ratio(SNR) and various reverberation environments, especially when there is noise similar to the target sound source.
- Target sound localization,
- Deep learning,
- Time-frequency units selection

FullText(HTML)

References(15)

References

MAY T, VAN DE PAR S, and KOHLRAUSCH A. A probabilistic model for robust localization based on a binaural auditory front-end[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2011, 19(1): 1–13. doi: 10.1109/TASL.2010.2042128

李如玮, 潘冬梅, 张爽, 等. 基于Gammatone滤波器分解的HRTF和GMM的双耳声源定位算法[J]. 北京工业大学学报, 2018, 44(11): 1385–1390. doi: 10.11936/bjutxb2017090015

LI Ruwei, PAN Dongmei, ZHANG Shuang, et al. Binaural sound source localization algorithm based on HRTF and GMM Under Gammatone filter decomposition[J]. Journal of Beijing University of Technology, 2018, 44(11): 1385–1390. doi: 10.11936/bjutxb2017090015

WOODRUFF J and WANG Deliang. Binaural localization of multiple sources in reverberant and noisy environments[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(5): 1503–1512. doi: 10.1109/TASL.2012.2183869

MA Ning, MAY T, and BROWN G J. Exploiting deep neural networks and head movements for robust binaural localization of multiple sources in reverberant environments[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2017, 25(12): 2444–2453. doi: 10.1109/TASLP.2017.2750760

MA Ning, GONZALEZ J A, and BROWN G J. Robust binaural localization of a target sound source by combining spectral source models and deep neural networks[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018, 26(11): 2122–2131. doi: 10.1109/TASLP.2018.2855960

WANG Yuxuan, HAN Kun, and WANG Deliang. Exploring monaural features for classification-based speech segregation[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2013, 21(2): 270–279. doi: 10.1109/TASL.2012.2221459

JIANG Yi, WANG Deliang, LIU Runsheng, et al. Binaural classification for reverberant speech segregation using deep neural networks[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014, 22(12): 2112–2121. doi: 10.1109/TASLP.2014.2361023

ZHANG Xueliang and WANG Deliang. Deep learning based binaural speech separation in reverberant environments[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2017, 25(5): 1075–1084. doi: 10.1109/TASLP.2017.2687104

WANG Deliang and CHEN Jitong. Supervised speech separation based on deep learning: An overview[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018, 26(10): 1702–1726. doi: 10.1109/TASLP.2018.2842159

WIERSTORF H, GEIER M, and SPORS S. A free database of head related impulse response measurements in the horizontal plane with multiple distances[C]. The Audio Engineering Society Convention 130, Berlin, Germany, 2011.

HUMMERSONE C, MASON R, and BROOKES T. Dynamic precedence effect modeling for source separation in reverberant environments[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2010, 18(7): 1867–1871. doi: 10.1109/TASL.2010.2051354

MA Ning, BROWN G J, and GONZALEZ J A. Exploiting top-down source models to improve binaural localisation of multiple sources in reverberant environments[C]. The 16th Annual Conference of the International Speech Communication Association, Dresden, Germany, 2015: 160–164.

COOKE M, BARKER J, CUNNINGHAM S, et al. An audio-visual corpus for speech perception and automatic speech recognition[J]. The Journal of the Acoustical Society of America, 2006, 120(5): 2421–2424. doi: 10.1121/1.2229005

MAY T, MA Ning, and BROWN G J. Robust localisation of multiple speakers exploiting head movements and multi-conditional training of binaural cues[C]. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing, Brisbane, Australia, 2015: 2679–2683.

MAY T. Robust speech dereverberation with a neural network-based post-filter that exploits multi-conditional training of binaural cues[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018, 26(2): 406–414. doi: 10.1109/TASLP.2017.2765819

Relative Articles

Supplements(0)

Cited By

Proportional views