Adaptive Voice Activity Detection Based on Long-Term Information

被引：0

作者：

Yang X.-K. ^{[1
]}

Qu D. ^{[1
]}

Zhang W.-L. ^{[1
]}

Yan H.-G. ^{[1
]}

机构：

[1] PLA Information Engineering University, Zhengzhou, 450001, Henan

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2018年 / 46卷 / 04期

关键词：

Adaptive; Auditory filter bank; Long-term information; Voice activity detection;

D O I：

10.3969/j.issn.0372-2112.2018.04.016

中图分类号：

学科分类号：

摘要：

The long-term information of speech signals shows excellent performances in the applications of voice activity detection. Six types of long-term information based on auditory filter banks are proposed through the non-linear spectral decomposition with three different auditory filters. Further, an adaptive voice activity detection algorithm based on these types of long-term information is proposed. Without additional training data, this algorithm use the data selecting from the test signals according to long-term information to train a speech/non-speech classifier, and classifies the current test signals using the speech/non-speech classifier frame by frame. Experiments on TIMIT dataset and NOISEX-92 dataset show that the algorithm improves the performance of VAD with higher accuracy and stronger robustness in low SNR noisy environments. The online experiments show that it can also obtain a good performance in real-time processing conditions. © 2018, Chinese Institute of Electronics. All right reserved.

引用

页码：878 / 885

页数：7

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