**6. Conclusion**

This chapter focuses on wavelet denoising. It starts with the introduction of two major noise estimation methods: Bayes estimation and Minimax estimation. In orthogonal bases, thresholding is a common method to remove noises. The estimations show that oscillations or ripples will be induced by hard thresholding. Nevertheless, the SNR of estimation with hard thresholding is higher than soft thresholding since the magnitude of coefficients decreases after soft thresholding. Then the thresholds that developed by different noise estimations are proposed. The larger threshold removes more noises but it generates greater estimation risk.

The wavelet denoising methods are usually realized by orthogonal decomposition. The most commonly used orthogonal decompositions are multi-resolution analysis and wavelet packet transform. The influence of wavelet decomposition algorithms, hard or soft thresholdings, and fixed or level-dependent thresholds are studied and compared. For different application, the optimal wavelet thresholding method should be considered carefully.

The wavelet transform is to use a few large magnitude coefficients to represent a signal. The selection of wavelet is another important factor that needs consideration. The properties, for example regularity and degree, of signal should be studied when choosing optimal wavelet that has matching features such as vanishing moments, size of support, and regularity.

Fig. 12. Estimation of irregular data, (a) original data, (b) noisy data (SNR=10.38dB), (c) estimation with 'db2' (SNR=21.68dB), (d) estimation with 'coif3' (SNR=20.61dB)

This chapter focuses on wavelet denoising. It starts with the introduction of two major noise estimation methods: Bayes estimation and Minimax estimation. In orthogonal bases, thresholding is a common method to remove noises. The estimations show that oscillations or ripples will be induced by hard thresholding. Nevertheless, the SNR of estimation with hard thresholding is higher than soft thresholding since the magnitude of coefficients decreases after soft thresholding. Then the thresholds that developed by different noise estimations are proposed. The larger threshold removes more noises but it generates greater

The wavelet denoising methods are usually realized by orthogonal decomposition. The most commonly used orthogonal decompositions are multi-resolution analysis and wavelet packet transform. The influence of wavelet decomposition algorithms, hard or soft thresholdings, and fixed or level-dependent thresholds are studied and compared. For different application, the optimal wavelet thresholding method should be considered

The wavelet transform is to use a few large magnitude coefficients to represent a signal. The selection of wavelet is another important factor that needs consideration. The properties, for example regularity and degree, of signal should be studied when choosing optimal wavelet that has matching features such as vanishing moments, size of support,

**6. Conclusion** 

estimation risk.

carefully.

and regularity.

## **7. References**


**5** 

*Spain* 

Begona García Zapirain,

Ibon Ruiz and Amaia Mendez *Deustotech Institute of Technology,* 

**Oesophageal Speech's Formants** 

*Deustotech-LIFE Unit, University of Deusto, Bilbao,* 

**Measurement Using Wavelet Transform** 

One of the most important concerns for the specialists in otorrinolaringologists and the patients who have suffer a laringectomie is a complex process for their rehabilitation. At the present, it is no available any advanced technique either for the learning or the evaluation of

Esophageal speech is characterized by its low intelligibility, which implies that its objective measurement parameters e.g. pitch, jitter, shimmer or HNR have values outside normal ranges [1]. One of the consequences of this fact is the impossibility of using speech recognizers, speech to text converters or any kind of automatic response device that requires

The here presented paper explains a work which is included in a research whose objective is to adapt speech controlled systems so that they can be used by people with vocal disorders.

Our research group has presented many works to the scientific community [2], [3], aimed to the improvement of esophageal speech quality by stabilizing the poles of the system which models the vocal tract with LPC. Nowadays the wavelet transform is being used in order to enhance the Harmonics to noise ratio. For this task, it is crucial to know accurately the

In this paper results of a new algorithm are presented, this algorithm uses Wavelets Transform as basis, but proposes a new technique to improve calculation accuracy. In order to evaluate this new technique a comparative between its results and the ones obtained with the LPC will be elaborated. As a reference for the comparative the results of analyzing the

The general objective of the chapter is the enhancement of esophageal speech quality in communications with humans and machines. This aim comes up of the low intelligibility of people who speak with esophageal voice after an operation called laryngectomy which is

Esophageal voices are the most grievous among these pathologies.

frequency values of formants in vowels [7].

carry out like treatment of larynx cancer [6].

FFT transform will be taken [4].

**1. Introduction** 

this process.

a speech signal.

Zhang H., Blackburn T. R., Phung B. T. & Sen D. (2007). A novel wavelet transform technique for on-line partial discharge measurements. 1. WT de-noising algorithm. *IEEE Transactions on Dielectrics and Electrical Insulation*, Vol. 14, No. 1, (February, 2007), pp. 3-14, ISSN 1070-9878
