Adaptive Dereverberation Using Multi-channel Linear Prediction with Deficient Length Filter
Li GJ(李冠君); Liang S(梁山); Nie S(聂帅); Liu WJ(刘文举)
2019
会议名称icassp
会议日期2019
会议地点英国
摘要

In almost all adaptive dereverberation algorithms based on the multi-channel linear prediction (MCLP) model, it is assumed that the filter length can cover the reverberation time. However, in many practical situations, a deficient length filter, whose length is less than the reverberation time, is employed in consideration of computational cost. A deficient length filter fails to fully model the late reverberation, resulting in degraded performance. In this paper, we present a new MCLP-based adaptive dereverberation algorithm to improve the dereverberation performance when using a deficient length filter. We introduce a gain and use the filter coefficients estimated from the previous frame to track the MCLP modeling errors of the current frame. The gain and the filter coeffi-cients are jointly optimized and solved by using an alternating minimization technique. Experimental results show the superiority of the proposed algorithm. The shorter the filter length is, the more advantageous the proposed algorithm is.

七大方向——子方向分类语音识别与合成
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/44843
专题多模态人工智能系统全国重点实验室_智能交互
作者单位Institute of Automation, Chinese Academy of Sciences
第一作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Li GJ,Liang S,Nie S,et al. Adaptive Dereverberation Using Multi-channel Linear Prediction with Deficient Length Filter[C],2019.
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