Page 15 - 《应用声学》2019年第6期
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第 38 卷 第 6 期                                                                       Vol. 38, No. 6
             2019 年 11 月                         Journal of Applied Acoustics                 November, 2019

             ⋄ 研究报告 ⋄



                基于稀疏表示和特征加权的离格双耳声源定位                                                                    ∗



                                   丁建策     1,2   厉 剑    1,2   郑成诗     1,2†  李晓东      1,2



                                      (1 中国科学院声学研究所噪声与振动重点实验室             北京   100190)
                                                (2 中国科学院大学      北京  100049)
                摘要    基于头相关传递函数数据库的传统双耳声源定位方法的定位角度往往被限定在头相关传递函数数据
                库的离散测量点上。当头相关传递函数数据库的测量方位角间隔较大时，这类算法的性能会显著下降，这就是
                典型的离格问题。该文提出了基于加权宽带稀疏贝叶斯学习的离格双耳声源定位算法。首先该算法建立离格
                双耳信号的稀疏表示模型，然后利用双耳相干与扩散能量比特征对各个频点进行加权以降低噪声和混响的影
                响，最后通过加权宽带稀疏贝叶斯学习方法估计离格声源的方位角。实验结果表明，该算法在各种复杂的声学
                环境下都有着较高的定位精度和鲁棒性，特别是提高了离格条件下的声源定位性能。
                关键词     离格双耳声源定位，稀疏表示，双耳相干与扩散能量比，宽带稀疏贝叶斯学习
                中图法分类号: TN9123           文献标识码: A          文章编号: 1000-310X(2019)06-0917-09
                DOI: 10.11684/j.issn.1000-310X.2019.06.002

                 Off-grid binaural sound source localization using sparse representation and

                                                   feature weighting


                             DING Jiance 1,2  LI Jian 1,2  ZHENG Chengshi 1,2  LI Xiaodong 1,2

                     (1 Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences,
                                                    Beijing 100190, China)
                                   (2 University of Chinese Academy of Sciences, Beijing 100049, China)

                 Abstract  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 off-
                 grid situations.
                 Key words Off-grid binaural sound source localization, Sparse representation, Coherent-to-diffuse power
                 ratio, Wideband sparse Bayesian learning


             2019-03-11 收稿; 2019-06-24 定稿
             国家自然科学基金项目 (61571435, 61801468)
             ∗
             作者简介: 丁建策 (1990- ), 男, 河南南阳人, 博士研究生, 研究方向: 信号与信息处理。
             † 通讯作者 E-mail: cszheng@mail.ioa.ac.cn
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