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Speech Enhancement using a Variable Level Decomposition DWT
National Academy Science Letters ( IF 1.1 ) Pub Date : 2020-08-16 , DOI: 10.1007/s40009-020-00983-3
Samba Raju Chiluveru , Manoj Tripathy

The wavelet transform shows a promising solution for a non-stationary signal. The denoising of a noisy speech signal carried with a wavelet thresholding technique. The noisy signal decomposed into different frequency bands, and this decomposition level (DL) decided independent of non-stationary noise. In this letter, a new DL detection procedure presented, and it decides the decomposition level based on signal energy and speech dominance. The proposed DL applies to the speech denoising model, and the obtained results compared with the unversal thresholding technique and the minimum mean square error algorithm. The performance of the enhanced speech signal measures with speech intelligibility measure (STOI), and speech quality measure (PESQ). The experimental results revealed that the proposed scheme outperforms the conventional methods in all SNR and non-stationary environments.



中文翻译:

使用可变水平分解DWT的语音增强

小波变换显示了一种用于非平稳信号的有希望的解决方案。小波阈值技术携带的语音信号的去噪。噪声信号分解为不同的频带,并且此分解级别(DL)决定于非平稳噪声。在这封信中,提出了一种新的DL检测程序,它根据信号能量和语音优势确定分解级别。提出的DL应用于语音去噪模型,并将得到的结果与通用阈值技术和最小均方误差算法进行比较。增强的语音信号措施具有语音清晰度措施(STOI)和语音质量措施(PESQ)的性能。

更新日期:2020-08-16
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