Digital signals analysis with the LPC method

This paper concerns the issue of signal analysis by the linear predictive method. It is not only about frequency analysis, that is obtaining the LPC spectrum and comparing it with the Fourier one, but also about analysing prediction coefficients themselves. We will also discuss the program made by the authors of this paper, which beside foregoing functionalities, enables vocal tract visualization, modelled on the basis of LPC coefficients

Formant paths tracking using Linear Prediction based methods

This paper focuses on formants as basic parameters for vowels recognition. There are used two different algorithms for formants finding based on the LP algorithm: spectral peak picking and root extraction algorithm - obtaining very good path estimations by each algorithm. Those methods are compared in a graphical form in our application ‘WaveBlaster’

Wavelet analysis of speech signal

This paper concerns the issue of wavelet analysis of signals by continuous and discrete wavelettransforms (CWT – Continous Wavelet Transform, DWT – Discrete Wavelet Transform). Themain goal of our work was to develop a program which, through the CWT and the DWT analyses,would obtain graph of time-scale changes and would transform it into the spectrum, that is a graphof frequency changes. In this program we also obtain spectra of Fourier Transform and LinearPrediction. Owing to this, we can compare the Wavelet Transform results to those from the FourierTransform and Linear Prediction

Kohonen networks application in speech analysis algorithms

This article presents the Kohonen network application in the speech analysis. The Authors have modified the traditional Kohonen network learning process like weights initialization, neurons reduction and neurons sorting. The results will be presented using authors' program - WaveBlaster

Utterance intonation imaging using the cepstral analysis

Speech intonation consists mainly of fundamental frequency, i.e. the frequency of vocal cord vibrations. Finding those frequency changes can be very useful — for instance, studying foreign languages where speech intonation is an inseparable part of a language (like grammar or vocabulary). In our work we present the cepstral algorithm for F0 finding as well as an application for facilitating utterance intonation learning

Utterance intonation imaging using the cepstral analysis

Speech intonation consists mainly of fundamental frequency, i.e. the frequency of vocal cord vibrations. Finding those frequency changes can be very useful — for instance, studying foreign languages where speech intonation is an inseparable part of a language (like grammar or vocabulary). In our work we present the cepstral algorithm for F0 finding as well as an application for facilitating utterance intonation learning

Time–frequency Analysis of the EMG Digital Signals

In the article comparison of time-frequency spectra of EMG signals obtained by the following methods: Fast Fourier Transform, predictive analysis and wavelet analysis is presented. The EMG spectra of biceps and triceps while an adult man was flexing his arm were analysed. The advantages of the predictive analysis were shown as far as averaging of the spectra and determining the main maxima are concerned. The Continuous Wavelet Transform method was applied, which allows for the proper distribution of the scales, aiming at an accurate analysis and localisation of frequency maxima as well as the identification of impulses which are characteristic of such signals (bursts) in the scale of time. The modified Morlet wavelet was suggested as the mother wavelet. The wavelet analysis allows for the examination of the changes in the frequency spectrum in particular stages of the muscle contraction. Predictive analysis may also be very useful while smoothing and averaging the EMG signal spectrum in time

Analysis of surface myoelectric signals by linear prediction method

The article presents a proposal to use linear prediction method for a quick analysis of surface myoelectric (EMG) signals. The spectra obtained with the linear prediction (LP) and Fourier methods were compared. The LP method allows for a precise determination of the location and amplitude of the spectrum maximum and observation of changes in muscle tension and contraction phases. EMG spectra of brachial biceps during flexion and extension of the forearm by four adults were analyzed. The optimal width of the time window for the averaging of motor unit action potentials that allows for the observation of changes during contraction was established. It has been found that maximum spectrum during flexion has a significantly higher frequency and amplitude than during the extension of the forearm

DISORDERED SOUND REPETITION RECOGNITION IN CONTINUOUS SPEECH USING CWT AND KOHONEN NETWORK

Kohonen network, automatic disorderds speech recognition, waveblaster, cwt, continuous wavelet transform, bark scale, sound repetitio