Page 62 - 201901

第 38 卷 第 1 期 Vol. 38, No. 1
2019 年 1 月 Journal of Applied Acoustics January, 2019

⋄ 研究报告 ⋄

偏度最大化多通道逆滤波语声去混响研究 ∗

郭 颖 1,2 彭任华 1 郑成诗 1† 李晓东 1

(1 中国科学院噪声与振动重点实验室 (声学研究所) 北京 100190)
(2 中国科学院大学 北京 100049)
摘要 房间混响会降低语声质量和语声可懂度。高阶统计量是衡量非高斯性的重要参量，基于语声非高斯特
性可实现语声去混响。该文提出一种基于高阶统计量的多通道语声去混响方法，该方法首次用多通道语声信
号线性预测残差的三阶统计量偏度构造代价函数，以去混响重建信号线性预测残差的偏度最大化为目标自适
应地更新逆滤波器，同时引入通道逆滤波和语声产生系统的联合估计。实验结果表明，该方法相较于已有的基
于线性预测残差四阶统计量峰度的方法具有更好的去混响效果，且对噪声具有更强的鲁棒性。
关键词 高阶统计量，偏度，线性预测，房间脉冲响应，逆滤波
中图法分类号: TN912.35 文献标识码: A 文章编号: 1000-310X(2019)01-0058-10
DOI: 10.11684/j.issn.1000-310X.2019.01.009

Maximum skewness-based multichannel inverse filtering for speech

dereverberation

GUO Ying 1,2 PENG Renhua 1 ZHENG Chengshi 1 LI Xiaodong 1

(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 Room reverberation often leads to the reduction of speech quality and speech intelligibility. Speech
dereverberation can be achieved by using non-Gaussian property of speech, where higher order statistics (HOS)
are typical measurements. This paper presents a method based on HOS for multichannel speech dereverbera-
tion. The cost function is constructed using the third-order statistics, namely skewness, of multichannel speech
signal linear prediction residuals, and then update the inverse filter adaptively by maximizing the skewness of
the linear prediction residuals of the reconstructed speech signal. Meanwhile, we introduce the joint estimation
of the channel’s inverse filter and the speech production system. Experimental results show that the proposed
method is superior to the method based on forth-order statistics, i.e. kurtosis, in terms of dereverberation and
robustness to the noise.
Key words Higher order statistics, Skewness, Linear prediction, Room impulse response, Inverse filtering

2018-04-03 收稿; 2018-09-03 定稿
国家自然科学基金项目 (61571435)
∗
作者简介: 郭颖 (1992- ), 女, 辽宁朝阳人, 硕士研究生, 研究方向: 信号与信息处理。
† 通讯作者 E-mail: cszheng@mail.ioa.ac.cn