A review on speech enhancement algorithms and why to combine with environment classification

Nidhyananthan, S. Selva; Kumari, R. Shantha Selva; Prakash, A. Arun

doi:10.1142/s0129183114300024

Cited by 4 publications

(5 citation statements)

References 42 publications

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“…The empirical mode decomposition (EMD) is a recently developed new effective data analysis technique for nonstationary signals and nonlinear systems has made an effective track for enhancement of speech studies [15].…”

Section: Emd Based Speech Enhancement Techniquesmentioning

confidence: 99%

“…EMD was a powerful tool recently developed for analysis data for non-stationary and nonlinear for speech enhancement [15]. The complex signal can be decompose into zero mean oscillating components by the data-adaptive decomposition method in the EMD is the intrinsic mode functions (IMFs).…”

Section: Emd Based Speech Enhancement Techniquesmentioning

confidence: 99%

“…Non-parametric approaches simply need an estimation of the noise spectrum. This spectrum can be predicted by the pause periods where the speaker is silent (single channel) or from a reference source (multichannel) [15].…”

Section: Introductionmentioning

confidence: 99%

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Emd Based Speech Enhancement Using Soft and Hard Threshold Techniques

Rudresh¹

2016

IJRET

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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.

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Section: Emd Based Speech Enhancement Techniquesmentioning

confidence: 99%

Section: Emd Based Speech Enhancement Techniquesmentioning

confidence: 99%

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Emd Based Speech Enhancement Using Soft and Hard Threshold Techniques

Rudresh¹

2016

IJRET

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“…However, it has quite good result when the noisy speech SNR is relatively high; above 15 dB [2]. The SS and other speech enhancement methods that are based on SS principal have ameliorated the decision directed (DD) methods in reducing the musical noise components [10][11][12][13]. Numerous algorithms that ameliorate the DD methods were suggested in [14].…”

Section: Introductionmentioning

confidence: 99%

Speech Enhancement Based on LWT and Artificial Neural Network and Using MMSE Estimate of Spectral Amplitude

Talbi¹,

Baazaoui²,

Bouhlel³

2021

Deep Learning Applications

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In this chapter, we will detail a new speech enhancement technique based on Lifting Wavelet Transform (LWT) and Artifitial Neural Network (ANN). This technique also uses the MMSE Estimate of Spectral Amplitude. It consists at the first step in applying the LWTto the noisy speech signal in order to obtain two noisy details coefficients, cD1 and cD2 and one approximation coefficient, cA2. After that, cD1 and cD2 are denoised by soft thresholding and for their thresholding, we need to use suitable thresholds, thrj,1≤j≤2. Those thresholds, thrj,1≤j≤2, are determined by using an Artificial Neural Network (ANN). The soft thresholding of those coefficients, cD1 and cD2, is performed in order to obtain two denoised coefficients, cDd1 and cDd2 . Then the denoising technique based on MMSE Estimate of Spectral Amplitude is applied to the noisy approximation cA2 in order to obtain a denoised coefficient, cAd2. Finally, the enhanced speech signal is obtained from the application of the inverse of LWT, LWT−1 to cDd1, cDd2 and cAd2. The performance of the proposed speech enhancement technique is justified by the computations of the Signal to Noise Ratio (SNR), Segmental SNR (SSNR) and Perceptual Evaluation of Speech Quality (PESQ).

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“…In different noise environments, the optimal parameters of speech enhancement algorithms are different. As investigated in [23], this prior environmental information was helpful to further improve the speech enhancement methods to achieve better noise suppression effect in specific noise environments. Therefore, some researchers started to design the speech enhancement algorithms by incorporating the prior noise information.…”

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confidence: 99%

Environmental Attention-Guided Branchy Neural Network for Speech Enhancement

et al. 2020

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The performance of speech enhancement algorithms can be further improved by considering the application scenarios of speech products. In this paper, we propose an attention-based branchy neural network framework by incorporating the prior environmental information for noise reduction. In the whole denoising framework, first, an environment classification network is trained to distinguish the noise type of each noisy speech frame. Guided by this classification network, the denoising network gradually learns respective noise reduction abilities in different branches. Unlike most deep neural network (DNN)-based methods, which learn speech reconstruction capabilities with a common neural structure from all training noises, the proposed branchy model obtains greater performance benefits from the specially trained branches of prior known noise interference types. Experimental results show that the proposed branchy DNN model not only preserved better enhanced speech quality and intelligibility in seen noisy environments, but also obtained good generalization in unseen noisy environments.for the performance improvement of DNN-based speech denoising. Generative adversarial networks (GAN) [22] have been another new solution to train the DNN model to learn noise suppression abilities. These methods aim to train a common DNN denoising model for all kinds of noise interference in different application scenarios.However, most speech products have their specific application scenarios. When the application scenario is determined, the types of noise interference are known. In different noise environments, the optimal parameters of speech enhancement algorithms are different. As investigated in [23], this prior environmental information was helpful to further improve the speech enhancement methods to achieve better noise suppression effect in specific noise environments. Therefore, some researchers started to design the speech enhancement algorithms by incorporating the prior noise information. In [24][25][26], the noise classification module was integrated into the Wiener filter and some statistical model-based speech estimators. Guided by the noise classification module for optimal parameter selection, the performance of traditional methods was further improved in prior known noisy environments. A similar idea was applied to the DNN-based denoising algorithms [27][28][29]. In the recent studies of [28,29], under the guidance of the noise classification unit, several independent DNN models were trained to give full play to their respective noise reduction capabilities in different noise environments. Although this "divide and conquer" strategy effectively improves the noise reduction performance, it also increases the storage burden and reduces the complementarity between different noises.In this work, we proposed a novel branchy neural network (BNN) framework to improve the storage burden and noise complementarity problem of separate training. There are two key modules working together in our proposed framework, a classification...

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A review on speech enhancement algorithms and why to combine with environment classification

Cited by 4 publications

References 42 publications

Emd Based Speech Enhancement Using Soft and Hard Threshold Techniques

Emd Based Speech Enhancement Using Soft and Hard Threshold Techniques

Speech Enhancement Based on LWT and Artificial Neural Network and Using MMSE Estimate of Spectral Amplitude

Environmental Attention-Guided Branchy Neural Network for Speech Enhancement

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