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Robust Voice Activity Detection Feature Design Based on Spectral Kurtosis

机译:基于谱峰度的鲁棒语音活动检测特征设计

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In traditional VAD algorithms, High Order Statistics (HOS) is usually used in time domain and limited to white noise case. In this paper, a spectral domain HOS feature called spectral kurtosis is introduced, on the bases of which an essential exploring to the different characters between speech and noise in spectral domain is carried out. By the introducing of ldquotime delayrdquo and double thresholds method, an effective VAD algorithm based on spectral kurtosis is proposed. Experiment results show that the proposed feature and VAD algorithm based on it has better performance than other ones such as short-term energy, entropy, cepstral distance, etc, especially when the SNR and types of noise are time varying, so it has more applications in speech signal processing.
机译:在传统的VAD算法中,高阶统计量(HOS)通常用于时域,并且仅限于白噪声情况。本文介绍了一种称为频谱峰度的频谱域居屋系统功能,在此基础上,对语音和噪声在频谱域中的不同特征进行了必要的探索。通过引入“时延”和“双阈值”方法,提出了一种基于频谱峰度的有效VAD算法。实验结果表明,所提出的特征和基于该特征的VAD算法具有比短期能量,熵,倒谱距离等其他特征更好的性能,尤其是在信噪比和噪声类型随时间变化的情况下,具有更多的应用前景。在语音信号处理中。

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