Speech Enhancement using Adaptive Empirical Mode Decomposition

N. Chatlani, J. Soraghan
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引用次数: 7

Abstract

Speech enhancement is performed in a wide and varied range of instruments and systems. In this paper, a novel approach to Speech Enhancement using Adaptive Empirical Mode Decomposition (SEAEMD) is presented. Spectral analysis of non-stationary signals can be performed by employing techniques such as the STFT and the Wavelet transform (WT), which use predefined basis functions. Empirical Mode Decomposition (EMD) performs very well in such environments. EMD decomposes a signal into a finite number of data-adaptive basis functions, called Intrinsic Mode Functions (IMFs). The new SEAEMD system incorporates this multi-resolution approach with adaptive noise cancellation (ANC) for effective speech enhancement on an IMF level, in stationary and non-stationary noise environments. A comparative performance study is included that compares the competitive method of conventional ANC to the robust SEAEMD system. The results demonstrate that the new system achieves significantly improved speech quality with a lower level of residual noise.
基于自适应经验模态分解的语音增强
语音增强是在各种各样的仪器和系统中进行的。本文提出了一种基于自适应经验模态分解(SEAEMD)的语音增强方法。非平稳信号的频谱分析可以通过使用STFT和小波变换(WT)等技术来执行,这些技术使用预定义的基函数。经验模态分解(EMD)在这种环境下表现很好。EMD将信号分解为有限个数据自适应基函数,称为内禀模态函数(IMFs)。新的SEAEMD系统将这种多分辨率方法与自适应噪声消除(ANC)结合在一起,在平稳和非平稳噪声环境中实现IMF级别的有效语音增强。比较性能研究包括比较传统的竞争方法的ANC和鲁棒SEAEMD系统。结果表明,新系统在较低的残余噪声水平下显著提高了语音质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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