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《Journal of Applied Acoustics》 2019-06
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Off-grid binaural sound source localization using sparse representation and feature weighting

DING Jiance;LI Jian;ZHENG Chengshi;LI Xiaodong;Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences;University of Chinese Academy of Sciences;  
Traditional binaural sound source localization(BSSL) techniques using measured head-related transfer function(HRTF) databases often suffer a typical off-grid problem, where their estimated azimuth angles are restricted at the measured azimuth angles of HRTF databases. When the interval of the measured azimuth angles is large, the performance of these techniques will degrade significantly. This paper proposes an off-grid BSSL algorithm based on weighted wideband sparse Bayesian learning. First, the algorithm establishes an off-grid sparse representation model. Then weighted values based on binaural coherent-to-diffuse power ratio(BCDR) for all frequency bins are calculated to reduce the impact of noise and reverberation. Finally, a weighted wideband sparse Bayesian learning algorithm is derived to solve the off-grid BSSL problem. Experimental results demonstrate that the proposed method can achieve higher localization accuracy and is more robust than the compared HRTF-based BSSL techniques in various acoustic environments, especially under the offgrid situations.
【Fund】: 国家自然科学基金项目(61571435 61801468)
【CateGory Index】: TN912.3;TP311.13
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