数据采集与处理2024，Vol.39Issue(5)：1135-1146,12.DOI:10.16337/j.1004-9037.2024.05.007
利用互子带滤波器和稀疏特性的多通道线性预测语音去混响方法
Multi-channel Linear Prediction for Speech Dereverberation Using Cross-Band Filters and Sparse Priors
摘要
Abstract
The multi-channel linear prediction(MCLP)is one of the most popular speech dereverberation methods.The band-to-band spectral subtraction model has been adopted by most existing studies to obtain the desired speech signal in each frequency band,but it neglects the interaction between different frequencies.This paper proposes a MCLP-based speech dereverberation method using the cross-band spectral subtraction model instead of the widely adopted band-to-band spectral subtraction model.The proposed model employs cross-band filters to account for the interactions between different frequencies.We model the desired signal using the complex generalized Gaussian(CGG)distribution.Compared with the Gaussian distribution,the CGG distribution can capture the sparse nature of speech signals using a suitable shape parameter.Within the maximum likelihood estimation framework,the speech dereverberation problem is formulated as an optimization problem involving the band-to-band and cross-band filters.An optimization algorithm with guaranteed convergence is derived based on the majorization-minimization method.A series of speech dereverberation experiments under various reverberation times,different channel numbers and different source-to-microphone distances demonstrate that the proposed method significantly outperforms traditional methods in terms of dereverberation performance.关键词
语音去混响/多通道线性预测/互子带滤波器/复广义高斯分布/替代最小化Key words
speech dereverberation/multi-channel linear prediction/cross-band filter/complex generalized Gaussian distribution/majorization minimization分类
信息技术与安全科学引用本文复制引用
康瑶,康坊,杨飞然..利用互子带滤波器和稀疏特性的多通道线性预测语音去混响方法[J].数据采集与处理,2024,39(5):1135-1146,12.基金项目
国家自然科学基金面上项目(62171438) （62171438）
北京市自然科学基金(4242013) （4242013）
中国科学院声学研究所自主部署"前沿探索"类项目(QYTS202111) （QYTS202111）
2023年度国家开放大学重点科研项目(Z23C0007). （Z23C0007）
