An Improved Wiener Filtering Algorithm Based On Dynamic Noise Power Spectrum Estimation

摘要：

The problem of enhancing speech degraded by uncorrelated additive noise, when only the noisy speech is available, has been widely studied in the past and it is still an active field of research. Wiener filter, which is the most fundamental approach, has been delineated in different forms and adopted in diversified applications. An improved wiener filtering algorithm is proposed in this study, which utilizes band-partitioning spectral entropy to achieve accurate and robust speech endpoint detection and a dynamic noise power spectrum is estimated for updating a priori SNR. Experimental results reveal that the proposed algorithm can extract the embedded speech segments from utterances containing a variety of background noise successfully.

作者: Zhao Lv Xiaopei Wu Mi Li

作者单位: The First Aeronautical College of Air-Force, Xinyang, Henan Province, China The Key Laboratory of In The Key Laboratory of Intelligent Computing & Signal Processing, Anhui University, Hefei, Anhui Prov The First Aeronautical College of Air-Force, Xinyang, Henan Province, China

会议类型: 国际会议

会议名称: The 4th International Conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-06-18（万方平台首次上网日期，不代表论文的发表时间）