THE ADVANTAGE OFTHEWAVELET TRANSFORM IN PROCESSING OF SPEECH SIGNALS

2021 ◽  
Vol 4 (3) ◽  
pp. 37-41
Author(s):  
Sayora Ibragimova ◽  
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Speech Processing ◽  
High Performance ◽  
Speech Sound ◽  
Digital Signal ◽  
Speech Signals ◽  
Multi Scale ◽  
Processing Of Speech Signals

This work deals with basic theory of wavelet transform and multi-scale analysis of speech signals, briefly reviewed the main differences between wavelet transform and Fourier transform in the analysis of speech signals. The possibilities to use the method of wavelet analysis to speech recognition systems and its main advantages. In most existing systems of recognition and analysis of speech sound considered as a stream of vectors whose elements are some frequency response. Therefore, the speech processing in real time using sequential algorithms requires computing resources with high performance. Examples of how this method can be used when processing speech signals and build standards for systems of recognition.Key words: digital signal processing, Fourier transform, wavelet analysis, speech signal, wavelet transform

Wavelet-based Segmentation and Feature Extraction of Heart Sounds for Intelligent PDA-based Phonocardiography

2007 ◽  
Vol 46 (02) ◽  
pp. 135-141 ◽  
Author(s):  
H. Nazeran
Keyword(s):  
Feature Extraction ◽  
Spectral Analysis ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Digital Signal ◽  
Heart Sounds ◽  
Segmentation Algorithm ◽  
Frequency Resolution ◽  
Time Frequency ◽  
Frequency Information

Summary Objectives : Many pathological conditions of the cardiovascular system cause murmurs and aberrations in heart sounds. Phonocardiography provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intracardiac phonocardiography combined with modern digital signal processing techniques has strongly renewed researchers' interest in studying heart sounds and murmurs.The aim of this work is to investigate the applicability of different spectral analysis methods to heart sound signals and explore their suitability for PDA-based implementation. Methods : Fourier transform (FT), short-time Fourier transform (STFT) and wavelet transform (WT) are used to perform spectral analysis on heart sounds. A segmentation algorithm based on Shannon energy is used to differentiate between first and second heartsounds. Then wavelet transform is deployed again to extract 64 features of heart sounds. Results : The FT provides valuable frequency information but the timing information is lost during the transformation process. The STFT or spectrogram provides valuable time-frequency information but there is a trade-off between time and frequency resolution. Waveletanalysis, however, does not suffer from limitations of the STFT and provides adequate time and frequency resolution to accurately characterize the normal and pathological heartsounds. Conclusions : The results show that the wavelet-based segmentation algorithm is quite effective in localizing the important components of both normal and abnormal heart sounds. They also demonstrate that wavelet-based feature extraction provides suitable feature vectors which are clearly differentiable and useful for automatic classification of heart sounds.

Multi-scale representation of tribological surfaces

2002 ◽  
Vol 216 (6) ◽  
pp. 463-479 ◽  
Author(s):  
P Podsiadlo ◽  
G. W. Stachowiak
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Numerical Methods ◽  
Limited Information ◽  
Wavelet Method ◽  
Multi Scale ◽  
Tribological Surfaces ◽  
Cohen's Class ◽  
Surface Data ◽  
Surface Topographies

Many numerical surface topography analysis methods exist today. However, even for the moderately complicated topography of a tribological surface these methods can provide only limited information. The reason is that tribological surfaces often exhibit a non-stationary and multi-scale nature while the numerical methods currently used work well with surface data exhibiting a stationary random process and provide surface descriptors closely related to a scale at which surface data were acquired. The suitability of different methods, including Fourier transform, windowed Fourier transform, Cohen's class distributions (especially the Wigner-Ville distribution), wavelet transform, fractal methods and a hybrid fractal-wavelet method, for the analysis of tribological surface topographies is investigated in this paper. The method best suited to this purpose has been selected.

Wavelet Analysis for Gas Turbine Fault Diagnostics

1997 ◽  
Vol 119 (4) ◽  
pp. 870-876 ◽  
Author(s):  
N. Aretakis ◽  
K. Mathioudakis
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Gas Turbines ◽  
Diagnostic Techniques ◽  
Diagnostic Information ◽  
Practical Application ◽  
Acoustic Measurements ◽  
Time Signals ◽  
Existing Data

The application of wavelet analysis to diagnosing faults in gas turbines is examined in the present paper. Applying the wavelet transform to time signals obtained from sensors placed on an engine gives information in correspondence to their Fourier transform. Diagnostic techniques based on Fourier analysis of signals can therefore be transposed to the wavelet analysis. In the paper the basic properties of wavelets, in relation to the nature of turbomachinery signals, are discussed. The possibilities for extracting diagnostic information by means of wavelets are examined, by studying the applicability to existing data from vibration, unsteady pressure, and acoustic measurements. Advantages offered, with respect to existing methods based on harmonic analysis, are discussed as well as particular requirements related to practical application.

Wavelet Analysis of the Pressure Fluctuations of Bottom Outlet of Kamal-Saleh Dam

2009 ◽  
Author(s):  
M. Liaghat ◽  
A. Abdollahi ◽  
F. Daneshmand ◽  
T. Liaghat
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Fourier Analysis ◽  
Spectral Characteristics ◽  
Pressure Fluctuations ◽  
Transient Phenomena ◽  
Service Gate ◽  
Stationary Signals ◽  
Bottom Outlet

Non-stationary signals are frequently encountered in a variety of engineering fields. The inability of conventional Fourier analysis to preserve the time dependence and describe the evolutionary spectral characteristics of non-stationary processes requires tools which allow time and frequency localization beyond customary Fourier analysis. The spectral analysis of non-stationary signals cannot describe the local transient features due to averaging over the duration of the signal [1]. The Fourier Transform (FT) and the short time Fourier transform (STFT) have been often used to measure transient phenomena. These techniques yield good information on the frequency content of the transient, but the time at which a particular disturbance in the signal occurred is lost [2, 3]. Wavelets are relatively new analysis tools that are widely being used in signal analysis. In wavelet analysis, the transients are decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave band of frequency. Wavelets do a very good job in detecting the time of the signal, but they give the frequency information in terms of frequency band regions or scales [4]. The main objective of this paper is to use the wavelet transform for analysis of the pressure fluctuations occurred in the bottom-outlet of Kamal-Saleh Dam. The “Kamalsaleh Dam” is located on the “Tire River” in Iran, near the Arak city. The Bottom Outlet of the dam is equipped with service gate and emergency gate. A hydraulic model test is conducted to investigate the dynamic behavior of the service gate of the outlet. The results of the calculations based on the wavelet transform is then compared with those obtained using the traditional Fast Fourier Transform.

scholarly journals COMPARATIVE CHARACTERISTICS OF SOFTWARE SYSTEMS FOR ANALYSIS AND PROCESSING OF SPEECH SIGNALS USING WAVELETS

2019 ◽  
Vol 45 (3) ◽  
pp. 103-113
Author(s):  
V. M. Dovgal ◽  
Min Zo Hein
Keyword(s):  
Wavelet Transform ◽  
Software Systems ◽  
Speech Signals ◽  
Speech Signal Processing ◽  
Transform Method ◽  
Software Complex ◽  
Advantages And Disadvantages ◽  
Software Packages ◽  
Processing Of Speech Signals ◽  
The Moment

Objectives. This article is devoted to the problem of processing and analysis of speech signals on the basis of the wavelet transform method, which has become one of the most relevant in recent years.Method. The growing relevance and undoubted practical value became the reason for the emergence of a large number of software systems that allow the processing of speech signals on the basis of this method. However, each of these systems has significant differences in the interface provided by the processing tools, functions, has a number of advantages and disadvantages. At the moment, a large number of manuals and recommendations for specific software packages have been written, but these materials are fragmented and unsystematic.Result. This article attempts to systematize the theoretical material and describe the similarities and differences, advantages and disadvantages of the three most popular software systems: 1) MATLAB 6.0/6.1/6.5 Wavelet Toolbox 2/2.1/2.2; 2) Mathcad; 3) Wavelet Explorer of Mathematica.Conclusion. This article will be useful for specialists dealing with the problem of speech signal processing using the wavelet transform method, as it contains material that has practical value, and will allow to facilitate the work of a specialist related to the selection of the optimal for the implementation of a specific task of the software complex.

scholarly journals A Comparative Study of Reassigned Conventional Wavelet Transform for Machinery Faults Detection

2015 ◽  
Vol 773-774 ◽  
pp. 90-94 ◽  
Author(s):  
Ahmed M. Abdelrhman ◽  
M. Salman Leong ◽  
Lim Meng Hee ◽  
Wai Keng Ngui
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Spectrum Analysis ◽  
Vibration Spectrum ◽  
Frequency Resolution ◽  
Transient Faults ◽  
Mother Wavelet ◽  
Time Frequency ◽  
Simulated Signal

Application of Fast Fourier Transform (FFT) in machinery faults detection is known to be only effective if fault is of repetitive in nature and considering severe. While minor and transient faults are usually remain undetected based on vibration spectrum analysis. Wavelet analysis is relatively new technique which is still suffered from inadequately in its time-frequency resolution. In this paper, ahmedrabak_time wavelet is proposed based on the wavelet reassignment technique for Morlet mother wavelet. The proposed wavelet analysis is compared to the conventional wavelet analysis for machinery faults detection based on simulated signal. The results showed that the proposed wavelet has a better resolution than conventional wavelet analysis which could clearly indicate the presence and the location of the fault.

scholarly journals Wavelet Analysis for Gas Turbine Fault Diagnostics

1996 ◽  
Author(s):  
N. Aretakis ◽  
K. Mathioudakis
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Gas Turbines ◽  
Diagnostic Techniques ◽  
Diagnostic Information ◽  
Practical Application ◽  
Acoustic Measurements ◽  
Time Signals ◽  
Existing Data

The application of wavelet analysis to diagnosing faults in Gas Turbines is examined in the present paper. Applying the Wavelet Transform to time signals obtained from sensors placed on an engine, gives information which is in correspondence to their Fourier Transform. Diagnostic techniques based on Fourier analysis of signals can therefore be transposed to the Wavelet analysis. In the paper the basic properties of wavelets, in relation to the nature of turbomachinery signals, are discussed. The possibilities for extracting diagnostic information by means of wavelets are examined, by studying the applicability to existing data from vibration, unsteady pressure and acoustic measurements. Advantages offered, with respect to existing methods based on harmonic analysis, are discussed as well as particular requirements related to practical application.

scholarly journals VLSI Implementation of Reconfigurable FFT Processor Using Vedic Mathematics

2015 ◽  
Vol 5 (2) ◽  
Author(s):  
Bharatha K. Babu ◽  
G. Nanthini
Keyword(s):  
Fourier Transform ◽  
Small Area ◽  
High Speed ◽  
High Performance ◽  
Digital Signal ◽  
Path Delay ◽  
Vedic Mathematics ◽  
Fft Processor ◽  
Wide Range ◽  
Single Path

Fast Fourier transform has been used in wide range of applications such as digital signal processing and wireless communications. In this we present a implementation of reconfigurable FFT processor using single path delay feedback architecture. To eliminate the use of read only memory’s (ROM’S). These are used to store the twiddle factors. To achieve the ROM-less FFT processor the proposed architecture applies the bit parallel multipliers and reconfigurable complex multipliers, thus consuming less power. The proposed architecture, Reconfigurable FFT processor based on Vedic mathematics is designed, simulated and implemented using VIRTEX-5 FPGA. Urdhva Triyakbhyam algorithm is an ancient Vedic mathematic sutra, which is used to achieve the high performance. This reconfigurable DIF-FFT is having the high speed and small area as compared with other conventional DIF-FFT

scholarly journals THE COMPARATIVE ANALYSIS THE FOURIER TRANSFORM, COSINE TRANSFORM AND WAVELET TRANSFORM AS A SPECTRAL ANALYSIS OF THE DIGITAL SPEECH SIGNALS

2015 ◽  
Vol 27 (3) ◽  
Author(s):  
Г. Ф. Конахович ◽  
О. І. Давлет’янц ◽  
О. Ю. Лавриненко ◽  
Д. І. Бахтіяров
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Comparative Analysis ◽  
Speech Signals ◽  
Cosine Transform ◽  
The Fourier Transform
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