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首页> 外文期刊>Journal of information and computational science >New De-noising Method for Speech Signal Based on Wavelet Entropy and Adaptive Threshold
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New De-noising Method for Speech Signal Based on Wavelet Entropy and Adaptive Threshold

机译：基于小波熵和自适应阈值的语音信号降噪新方法

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摘要

Aiming at the problem of speech signal de-noising, this paper presents an adaptive threshold de-noising method based on wavelet entropy. The method decomposes speech signal with noise by wavelet transform, and calculates wavelet entropy of the decomposed signal in each wavelet sub-interval. It combines the wavelet entropy with adaptive threshold to determine the threshold of high frequency coefficients. Compromised index threshold function is proposed to denoise the speech signal, and then the denoised signal is reconstructed. Finally, the paper compares the de-noising performance of the proposed threshold method, minimaxi threshold method, sqtwolog threshold method, and rigrsure threshold method. The simulation results show that when the input Signal-to-noise Ratio (SNR) is 7 dB, the output SNR with the proposed threshold de-noising method is the largest, and the input and output SNR curve is higher than what the other three kinds of threshold de-noising methods have, which proves that this method has better de-noising performance.
机译：针对语音信号降噪问题，提出了一种基于小波熵的自适应阈值降噪方法。该方法通过小波变换对语音信号进行噪声分解，并在每个小波子间隔中计算分解后信号的小波熵。它将小波熵与自适应阈值相结合，以确定高频系数的阈值。提出了折衷索引阈值函数对语音信号进行降噪，然后对降噪后的信号进行重构。最后，比较了所提出的阈值方法，最小极大值阈值方法，平方对数阈值方法和严格阈值方法的去噪性能。仿真结果表明，当输入信噪比（SNR）为7 dB时，采用阈值去噪方法的输出SNR最大，输入和输出SNR曲线均高于其他三个。阈值降噪方法有很多种，证明了该方法具有较好的降噪性能。

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