Wavelet based speech enhancement using two different threshold-based denoising algorithms
Wavelet based speech enhancement using two different threshold-based denoising algorithms
复制标题
使用两种不同的基于阈值的去噪算法进行基于小波的语音增强
DOI：
10.1109/ccece.2004.1345019
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
C. Gargour
中科院分区：
文献类型：
--
作者：
A. Lallouani;M. Gabrea;C. Gargour
本文提出了一种基于小波变换的语音去噪技术，它是将/SPLu/-律阈值和软阈值算法相结合得到的。去噪是在尽可能地去除噪声和保持信号完整性之间的折衷。为了很好地实施这一折衷方案，我们制定了以下程序。要去噪的信号使用小波包进行分解，直到使用DB11小波达到第七级。除应用软阈值的两个较低子带的那些子带系数外，/SPLu/-律阈值被应用于所有最终分解级子带系数。为了评估所提方法的性能，使用了来自TIMIT数据库的干净的语音数据集，该数据集被粉色噪声破坏，信噪比水平从5到15分贝。结果表明，我们的方法比单独使用的两种组合方法的结果要好。
In this paper, we present a wavelet-based speech denoising technique obtained by the combination of the /spl mu/-law thresholding and the soft thresholding algorithm. Denoising is a compromise between the removal of the largest possible amount of noise and the preservation of signal integrity. To achieve a good implementation of this compromise we purpose the following procedure. The signal to be denoised is decomposed using wavelet packets up to the seventh level using DB11 wavelets. The /spl mu/-law thresholding is applied to all the final decomposition level subband coefficients except those of the two lower subbands on which soft thresholding is applied. To evaluate the performance of the proposed method, a clean speech dataset from the TIMIT database, corrupted with pink noise, for SNR levels ranging from 5 to 15 dB has been utilized. It has been found that the results obtained by our method are better than those given by each one of the two combined methods used separately.