A new approach for speech enhancement based on the adaptive thresholding of the wavelet packets

Ghanbari, Yasser; Karami, Mohammadreza

doi:10.1016/j.specom.2005.12.002

Cited by 111 publications

(62 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The many research were used the wavelet thresholding for speech enhancement [8]. The main objective of this work is to propose a new wavelet threshoding algorithm based on the adaptive threshoding with low complexity and to check property of signal that it is suitable for any threshold function (see detail the section 2).…”

Section: Threshold Considerationmentioning

confidence: 99%

“…The denoising method is used wavelet transform and wavelet thresholding algorithm for reduce noise [7,8]. There are modified of the hard thresholding [8] and soft thresholding [7].…”

Section: Introductionmentioning

confidence: 99%

“…Many researches have evaluated result of denoising with signal-to-noise ratio [2,10], segmental signal-to-noise ratio [2,11,12], Mean Squared Error (MSE) [7,13], and used realistic noisy environment [2,8,[11][12][13]. Furthermore, the denosing method is applied to various kinds for example [13,14] proposed denoising base on wavelet and [8] are proposed new speech enhancement system using wavelet thresholding algorithm with modified of the hard thresholding function which sets the coefficients of wavelet to zero when lower than the threshold value because of time-frequency discontinuities in the enhanced speech spectrum and used wavelet was "db8". There are evaluated sound of speech under white Gaussian noise, pink noise, [10] is proposed a novel thresholding function base on wavelet is more elastic than the soft and hard thresholding.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Wavelet Denoising Method for Noise Threshold

Jangjit¹,

Ketcham²

2017

View full text Add to dashboard Cite

Abstract. This study is proposed a new adaptive threshold based on noisy speech for each sub-bands with low complex and it is suitability for range of human hearing and range of hearing test. A new method is used wavelet 1-D experimental signal for denoising. It provided the optimal adaptive threshold of three sub-band with applies to the detail coefficients. The speech enhancement is used of threshoding on the adpated wavelet coefficients, and the results are compared a variety of noisy speech and four well-known benchmark signals. The results, measured objectively by Signal-to-Noise ratio (SNR) and Mean Square Error (MSE), are given for additive white Gaussian noise as well as two different types of noisy environment. The new method called Adaptive Thresholding with Mean for hybrid Denoising method of hard and soft function (ATMDe) and applied to hearing loss and it is found that it increases the signal-to-noise ratio by more than 114 % and decreases the mean-square-error (MSE). The result of new method with SNR and MSE is higher than standard denoising methods. Hence, the new method was found that has good performance and adaptive threshold value is better than other methods.

show abstract

Section: Threshold Considerationmentioning

confidence: 99%

“…The denoising method is used wavelet transform and wavelet thresholding algorithm for reduce noise [7,8]. There are modified of the hard thresholding [8] and soft thresholding [7].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Wavelet Denoising Method for Noise Threshold

Jangjit¹,

Ketcham²

2017

View full text Add to dashboard Cite

show abstract

“…Moreover, it has a constant parameter in www.intechopen.com order to apply a nonlinear function to the threshold value. When is 3, the good results in speech signal are obtained (Ghanbari & Karami-Mollaei, 2006), which can be expressed as…”

Section: ) Qian Function (Qin)mentioning

confidence: 99%

The Usefulness of Wavelet Transform to Reduce Noise in the SEMG Signal

Phinyomark¹,

Phukpattaranont²,

Limsakul³

2012

EMG Methods for Evaluating Muscle and Nerve Function

View full text Add to dashboard Cite

“…4. The VAD method proposed in [22][23] to identify both voice and silence zones has been implemented using Texas instruments TMS320C6713 floating point digital signal processor operating at 225MHz to check the performance of speech de-noising in real time.…”

Section: Introduction Of Tlbo Algorithmmentioning

confidence: 99%

A Heuristic Speech De-noising with the aid of Dual Tree Complex Wavelet Transform using Teaching-Learning Based Optimization

Yugandhar¹,

Nayak²

2016

IJET

View full text Add to dashboard Cite

Abstract-In our present work, we propose a nature inspired population based speech enhancement technique to find the dynamic threshold value using Teaching-Learning Based Optimization (TLBO) algorithm by using shift invariant property of dual tree complex wavelet transform (DT-CWT). The performance of these proposed methods are evaluated in terms of Perceptual Evaluation of Speech Quality (PESQ) and Peak Signal to Noise Ratio (PSNR). Speech quality of different speech waves are compared for two level wavelet packet decomposition and dual tree wavelet transform using soft threshold. The speech qualities of the waves are better than the other available articles in the literature. Keyword-Speech enhancement, Dual tree complex wavelet transform, Teaching-Learning-based optimization, Perceptual Evaluation of Speech Quality I. INTRODUCTION In many speech related systems, the original speech signal is contaminated with some interference sources. The interference source, i.e the noise signal degrades the quality of the original speech. The speech signal is affected by wide-band noise in the form of white or colored noise and a period noise such as hum noise. The most common type of noise in communication channels is the additive wide band Gaussian noise. Speech enhancement aims at improving the performance of speech communication systems in noisy environments. Speech enhancement may be applied, for example, to a mobile radio communication system, a speech recognition system, a set of low quality recordings, or to improve the performance of aids for the hearing impaired. Several methods [1]- [6] have been proposed in the literature for the enhancement of degraded speech. A majority of these methods can be grouped into spectral processing and temporal processing methods. In the spectral processing methods, degraded speech is processed in the frequency domain mostly using Fourier transform for achieving speech enhancement. In the temporal processing methods, the processing is done in the time domain. Most speech enhancement methods improve the quality of the signal but degrade its intelligibility of the speech. Performance measures like PSNR and PESQ are widely used as the performance of the evolution criterion. For elimination of the Gaussian back ground noise in the communication channels, we have been implementing adaptive thresholding technique using TLBO optimization. The TLBO algorithm [7]-[9] is a global optimization, population based iterative learning mechanism that exhibits some common characteristics with other evolutionary computation (EC) techniques like (GA) [10], Particle Swarm Optimization (PSO) [11], Differential Evolution (DE) [12], and Artificial Bee Colony (ABC) [13]. The TLBO algorithm does not require any algorithm-specific control parameters like mutation and crossover as in genetic algorithm. The TLBO methods provide the learning mechanism in adaptive models. The organization of this paper is as follows. Section II presents speech de-nosing using wavelet thresholding. Section III discusses introduct...

show abstract

A new approach for speech enhancement based on the adaptive thresholding of the wavelet packets

Cited by 111 publications

References 13 publications

A New Wavelet Denoising Method for Noise Threshold

A New Wavelet Denoising Method for Noise Threshold

The Usefulness of Wavelet Transform to Reduce Noise in the SEMG Signal

A Heuristic Speech De-noising with the aid of Dual Tree Complex Wavelet Transform using Teaching-Learning Based Optimization

Contact Info

Product

Resources

About