Wavelet speech enhancement based on the Teager energy operator

Bahoura, Mohammed; Rouat, Jean

doi:10.1109/97.889636

Cited by 168 publications

(96 citation statements)

References 13 publications

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“…The performance of the proposed speech enhancement system based on time-frequency adapted WCTs is evaluated in various adverse conditions and compared to those of the others [9], [11]. It has been shown by experiments that even though the SNRs are very similar at the output of the enhancement system, the listening test and speech spectrograms can produce very divergent results.…”

Section: The Evaluation Of Speech Enhancementmentioning

confidence: 99%

“…However, the method may not work well in enhancing colored-noise corrupted signal. Since then, adaptive wavelet-based methods in speech enhancement have been widely developed [9], [11]. They utilize variant WCT to improve the performance of speech enhancement.…”

Section: Introductionmentioning

confidence: 99%

“…They utilize variant WCT to improve the performance of speech enhancement. Bahoura et al [11] proposed a method of threshold adaptation in time domain. Utilizing a Teager energy operator (TEO) to improve the discriminability of whether a speech frame is speech-dominated or noise-dominated.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Wavelet-Based Denoising Algorithm for Enhancing Speech in Non-stationary Noise Environment

Wang¹

2010

IEICE Trans. Inf. & Syst.

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SUMMARYTraditional wavelet-based speech enhancement algorithms are ineffective in the presence of highly non-stationary noise because of the difficulties in the accurate estimation of the local noise spectrum. In this paper, a simple method of noise estimation employing the use of a voice activity detector is proposed. We can improve the output of a wavelet-based speech enhancement algorithm in the presence of random noise bursts according to the results of VAD decision. The noisy speech is first preprocessed using bark-scale wavelet packet decomposition (BSWPD) to convert a noisy signal into wavelet coefficients (WCs). It is found that the VAD using bark-scale spectral entropy, called as BS-Entropy, parameter is superior to other energy-based approach especially in variable noise-level. The wavelet coefficient threshold (WCT) of each subband is then temporally adjusted according to the result of VAD approach. In a speech-dominated frame, the speech is categorized into either a voiced frame or an unvoiced frame. A voiced frame possesses a strong tone-like spectrum in lower subbands, so that the WCs of lower-band must be reserved. On the contrary, the WCT tends to increase in lower-band if the speech is categorized as unvoiced. In a noise-dominated frame, the background noise can be almost completely removed by increasing the WCT. The objective and subjective experimental results are then used to evaluate the proposed system. The experiments show that this algorithm is valid on various noise conditions, especially for color noise and non-stationary noise conditions.

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Section: The Evaluation Of Speech Enhancementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Adaptive Wavelet-Based Denoising Algorithm for Enhancing Speech in Non-stationary Noise Environment

Wang¹

2010

IEICE Trans. Inf. & Syst.

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“…In the transform domain, speech enhancement methods commonly use amplitude subtraction based soft thresholding defined by [34,35] …”

Section: Soft-thresholding With Dct (Sdct)mentioning

confidence: 99%

Empirical Mode Decomposition for Advanced Speech Signal Processing

Molla

Das

Hamid

et al. 2013

Journal of Signal Processing

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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.

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“…We propose 1) to perform a time adaptation of the thresholds based on the modulation defined with a nonlinear mask that is based on the Teager Energy Operator (Bahoura and Rouat, 2001a) -that we call TA -and 2) to extend the level-dependent threshold (Bahoura and Rouat, 2001b) to the wavelet packet decomposition -that we call TSA or TSA2 depending on the version being used.…”

Section: Contextmentioning

confidence: 99%

Wavelet speech enhancement based on time–scale adaptation

Bahoura

Rouat

2006

Speech Communication

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We propose a new speech enhancement method based on time and scale adaptation of wavelet thresholds. The time dependency is introduced by approximating the Teager Energy of the wavelet coefficients, while the scale dependency is introduced by extending the principle of level dependent threshold to Wavelet Packet Thresholding.This technique does not require an explicit estimation of the noise level or of the apriori knowledge of the SNR, as is usually needed in most of the popular enhancement methods. Performance of the proposed method is evaluated on speech recorded in real conditions (plane, sawmill, tank, subway, babble, car, exhibition hall, restaurant, street, airport, and train station) and artificially added noise. MELscale decomposition based on wavelet packets is also compared to the common wavelet packet scale.Comparison in terms of Signal-to-Noise Ratio (SNR) is reported for time adaptation and time-scale adaptation thresholding of the wavelet coefficients thresholding. Visual inspection of spectrograms and listening experiments are also used to support the results. Hidden Markov Models Speech recognition experiments are conducted on the AURORA-2 database and show that the proposed method improves the speech recognition rates for low SNRs.

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Wavelet speech enhancement based on the Teager energy operator

Cited by 168 publications

References 13 publications

An Adaptive Wavelet-Based Denoising Algorithm for Enhancing Speech in Non-stationary Noise Environment

An Adaptive Wavelet-Based Denoising Algorithm for Enhancing Speech in Non-stationary Noise Environment

Empirical Mode Decomposition for Advanced Speech Signal Processing

Wavelet speech enhancement based on time–scale adaptation

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