EMD-Based Filtering (EMDF) of Low-Frequency Noise for Speech Enhancement (original) (raw)
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EURASIP Journal on Advances in Signal Processing, 2008
In this study, two new approaches for speech signal noise reduction based on the empirical mode decomposition (EMD) recently introduced by Huang et al. (1998) are proposed. Based on the EMD, both reduction schemes are fully data-driven approaches. Noisy signal is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs), using a temporal decomposition called sifting process. Two strategies for noise reduction are proposed: filtering and thresholding. The basic principle of these two methods is the signal reconstruction with IMFs previously filtered, using the minimum mean-squared error (MMSE) filter introduced by I. Y. Soon et al. (1998), or thresholded using a shrinkage function. The performance of these methods is analyzed and compared with those of the MMSE filter and wavelet shrinkage. The study is limited to signals corrupted by additive white Gaussian noise. The obtained results show that the proposed denoising schemes perform better than the MMSE filter and wavelet approach.
EMD BASED SPEECH ENHANCEMENT USING SOFT AND HARD THRESHOLD TECHNIQUES
In last few decades many attempts have been made on speech signals to eliminates the noise. Purpose of use any speech enhancement algorithm is to eliminate noises in variety of environments; most prominent of which are telecommunication applications. The purpose of this paper is to development of a novel speech enhancement algorithm which offers superior noise reduction over current methods. This Research paper work demonstrates a novel time domain speech enhancement algorithm for speech signals called empirical mode decomposition (EMD). EMD decomposes the speech signal corrupted by noise signal into a finite number of band limited signals known as intrinsic mode functions (IMFs), using iterative procedure called sifting process. These IMFs are denoised by using two different techniques first method is IMFs thresholding method or direct method of speech enhancement and another technique is IMF frame based method are discussed in this work. Both the methods use soft and hard threshold techniques for denoising the IMFs which are obtained from EMD. These algorithms are implemented empirically by using MATLAB software on real time speech data. Experimental Results shows that IMFs frame based method superior than direct method this can be tested by adding noises with the different SNR values to the clean speech.
Speech enhancement in EMD domain using spectral subtraction and Wiener filter
— This paper proposes a technique in Empirical Mode Decomposition (EMD) domain to enhance the signal. The noisy signal is decomposed, by EMD, into approximation and detail which are filtered separately using spectral subtraction and Wiener filter. Therefore, the main idea of the proposed approach is to filter the shorter scale IMF (detail) by Wiener filter, which are noise dominated, and filter the approximation using spectral subtraction technique. In fact, the filtering of the approximation by the same filter (Wiener) will introduce signal distortion rather than a noise reduction. Thus, the performance of this method is to construct linearly the original signal without loss of the useful information. The study is limited to signals corrupted by additive white Gaussian noise.
An Improved Speech De-noising Method based on Empirical Mode Decomposition
Generally, Speech enhancement aims to improve speech quality and intelligibility of a noise contaminated speech signal by using various signal processing approaches. Removal of a noise from a noisy speech is a common problem; already a vast research was carried out in earlier. However, due to the characteristics of various types of noises, the approaches proposed in earlier are not applicable for all types of noises. In addition, the earlier approaches didn't focus on the non-linear and non-stationary characteristics on noise environments. EMD is a filtering approach performs efficiently for non-stationary environments. This paper proposes a novel EMDF approach with the inspiration of thresholding to remove the noise from noisy speech sample. The proposed approach also developed a method to select the IMF index for separating the residual low-frequency noise components from the speech estimate, based on the IMF statistics. An experimental study was also done on various types of noise contaminated speech samples like babble noise, restaurant noise and car interior noise at various strengths.
EMD-based noise estimation and tracking (ENET) with application to speech enhancement
2009 17th European Signal Processing Conference, 2009
Speech enhancement from measured speech signals is fundamental in a wide range of instruments. It relies on a noise estimate which can be obtained using techniques such as the minimum statistics (MS) approach. In this paper, a novel approach for Empirical Mode Decomposition (EMD) based noise estimation and tracking (ENET) is presented with application to speech enhancement. Spectral analysis of non-stationary signals such as speech is performed effectively using EMD. The Improved Minima Controlled Recursive Averaging (IMCRA) that evolved from MS has been shown to be effective in non-stationary environments. ENET is able to use EMD in a novel way to estimate the noise spectrum more accurately than IMCRA and enhance speech more effectively than conventional log-MMSE approaches. A comparative performance study is included that demonstrates that it achieves improved speech quality than a conventional log-MMSE filtering approach with better noise estimation, even during periods of strong...
Speech enhancement using empirical mode decomposition and the Teager–Kaiser energy operator
The Journal of the Acoustical Society of America, 2014
In this paper a speech denoising strategy based on time adaptive thresholding of intrinsic modes functions (IMFs) of the signal, extracted by empirical mode decomposition (EMD), is introduced. The denoised signal is reconstructed by the superposition of its adaptive thresholded IMFs. Adaptive thresholds are estimated using the Teager-Kaiser energy operator (TKEO) of signal IMFs. More precisely, TKEO identifies the type of frame by expanding differences between speech and non-speech frames in each IMF. Based on the EMD, the proposed speech denoising scheme is a fully data-driven approach. The method is tested on speech signals with different noise levels and the results are compared to EMD-shrinkage and wavelet transform (WT) coupled with TKEO. Speech enhancement performance is evaluated using output signal to noise ratio (SNR) and perceptual evaluation of speech quality (PESQ) measure. Based on the analyzed speech signals, the proposed enhancement scheme performs better than WT-TKEO and EMD-shrinkage approaches in terms of output SNR and PESQ. The noise is greatly reduced using time-adaptive thresholding than universal thresholding. The study is limited to signals corrupted by additive white Gaussian noise.
EMD based soft-thresholding for speech enhancement
2007
This paper introduces a novel speech enhancement method based on Empirical Mode Decomposition (EMD) and softthresholding algorithms. A modified soft thresholding strategy is adapted to the intrinsic mode functions (IMF) of the noisy speech. Due to the characteristics of EMD, each obtained IMF of the noisy signal will have different noise and speech energy distribution, thus will have a different noise variance. Based on this specific noise variance, by applying the proposed thresholding algorithm to each IMF separately, it is possible to effectively extract the existing noise components. The experimental results suggest that the proposed method is significantly more effective in removing the noise components from the noisy speech signal compared to recently reported techniques. The significantly better SNR improvement and the speech quality prove the superiority of the proposed algorithm.
Noise filtering using empirical mode decomposition
2007
In this paper a noise filtering method using the Empirical Mode Decomposition (EMD) is proposed. The noisy signal is decomposed into oscillatory components called Intrinsic Mode Functions (IMFs) using a process referred to as sifting. The basic idea of the proposed scheme is the partial reconstruction of the signal using the IMFs corresponding to the most important structures of the signal (low frequency modes). A new criterion is proposed to determine the IMF after which the energy distribution of the important structures of the signal overcomes that of the noise and that of the high frequency components of the signal. The method is tested on simulated and real signals.
Empirical Mode Decomposition for Advanced Speech Signal Processing
Journal of Signal Processing, 2013
Empirical mode decomposition (EMD) is a newly developed tool to analyze nonlinear and non-stationary signals. It is used to decompose any signal into a finite number of time varying subband signals termed as intrinsic mode functions (IMFs). Such data adaptive decomposition is recently used in speech enhancement. This study presents the concept of EMD and its application to advanced speech signal processing paradigms including speech enhancement by soft-thresholding, voiced/unvoiced (V/Uv) speech discrimination and pitch estimation. The speech processing is frequently performed in the transformed domain and the transformation is usually achieved by traditional signal analysis techniques i.e. Fourier and wavelet transformations. These analysis methods employ priori basis function and it is not suitable for data adaptive analysis for non-stationary signal like speech. Recently, EMD is taken much attention for speech signal processing in data adaptive way. Several EMD based potential soft-thresholding algorithms for speech enhancement are discussed here. The V/Uv discrimination is an important concern in speech processing. It is usually performed by using acoustic features. The training data is used to determine the threshold for classification. The EMD based data adaptive thresholding approach is developed for V/Uv discrimination without any training phase. Noticeable improvement is achieved with the application of EMD in pitch estimation of noisy speech signals. The related experimental results are also presented to realize the effectiveness of EMD in advanced speech processing algorithms.
Application of Variational Mode Decomposition on Speech Enhancement
Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, 2017
Enhancement of speech signal and reduction of noise from speech is still a challenging task for researchers. Out of many methods signal decomposition method attracts a lot in recent years. Empirical Mode Decomposition (EMD) has been applied in many problems of decomposition. Recently Variational Mode Decomposition (VMD) is introduced as an alternative to it that can easily separate the signals of similar frequencies. This paper proposes the signal decomposition algorithm as VMD for denoising and enhancement of speech signal. VMD decomposes the recorded speech signal into several modes. Speech contaminated with different types of noise is adaptively decomposed into various components is said to be Intrinsic Mode Functions (IMFs) by sifting process as in Empirical Mode decomposition (EMD) method. Next to it the denoising technique is applied using VMD. Each of the decomposed modes is compact. The simulation result shows that the proposed method is well suited for the speech enhancement and removal of noise by restoring the original signal.