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1
Content available Analysis of surface myoelectric signals by linear prediction method
Suszyński W., Kuniszyk-Jóźkowiak W., Jaszczuk J., Codello I., Stęgierski R., Kuczyński K.
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
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2016
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Vol. 16, no. 1
62--70
EN
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.
2
Content available Automatic prolongation recognition in disordered speech using CWT and Kohonen network
Codello I., Kuniszyk-Jóźkowiak W., Smołka E., Kobus A.
Journal of Medical Informatics & Technologies
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2012
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Vol. 20
137--144
EN
Automatic disorder recognition in speech can be very helpful for the therapist while monitoring therapy progress of the patients with disordered speech. In this article we focus on prolongations. We analyze the signal using Continuous Wavelet Transform with 18 bark scales, we divide the result into vectors (using windowing) and then we pass such vectors into Kohonen network. Quite large search analysis was performed (5 variables were checked) during which, recognition above 90% was achieved. All the analysis was performed and the results were obtained using the authors' program - "WaveBlaster". It is very important that the recognition ratio above 90% was obtained by a fully automatic algorithm (without a teacher) from the continuous speech. The presented problem is part of our research aimed at creating an automatic prolongation recognition system.
3
Content available Time–frequency Analysis of the EMG Digital Signals
Kuniszyk-Jóźkowiak W., Jaszczuk J., Sacewicz T., Codello I.
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
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2012
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Vol. 12, no. 2
20--25
EN
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.
4
Content available Automatic disordered sound repetition recognition in continuous speech using CWT and kohonen network
Codello I., Kuniszyk-Jóźkowiak W., Smołka E., Kobus A.
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
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2012
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Vol. 12, no. 2
39--48
EN
Automatic disorders recognition in speech can be very helpful for a therapist while monitoring therapy progress of patients with disordered speech. This article is focused on sound repetitions. The signal is analyzed using Continuous Wavelet Transform with 16 bark scales. Using the silence finding algorithm, only speech fragments are automatically found and cut. Each cut fragment is converted into a fixed-length vector and passed into the Kohonen network. Finally, the Kohonen winning neuron result is put on the 3-layer perceptron. Most of the analysis was performer and the results were obtained using the authors’ program WaveBlaster. We use the STATISTICA package for finding the best perceptron which was then imported back into WaveBlaster and used for automatic blockades finding. The problem presented in this article is a part of our research work aimed at creating an automatic disordered speech recognition system.
5
Content available Disordered sound repetition recognition in continuous speech using CWT and Kohonen network
Codello I., Kuniszyk-Jóźkowiak W., Smołka E., Kobus A.
Journal of Medical Informatics & Technologies
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2011
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Vol. 17
123--130
EN
Automatic disorders recognition in speech can be very helpful for therapist while monitoring therapy progress of patients with disordered speech. This article is focused on sound repetitions. The signal is analyzed using Continuous Wavelet Transform with 16 bark scales, the result is divided into vectors and passed into Kohonen network. Finally, the Kohonen winning neuron result is put on the 3-layer perceptron. The recognition ratio was increased by about 20% by adding a modification into the Kohonen network training process as well as into CWT computation algorithm. All the analysis was performed and the results were obtained using the authors' program ”WaveBlaster“, The problem presented in this article is a part of our research work aimed at creating an automatic disordered speech recognition system.
6
Content available A new elliptical model of the vocal tract
Kobus A., Kuniszyk-Jóźkowiak W., Smołka E., Suszyński W., Codello I.
Journal of Medical Informatics & Technologies
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2011
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Vol. 17
131--139
EN
In this paper a new model of the vocal tract is proposed. It is based on elliptical cylinders. It uses the vocal tract model based on PARCOR coefficients and midsaggital measurements of the voice tube. PARCOR coefficients were obtained from linear prediction coefficients which had been obtained by Levinson-Durbin method. Midsaggital lengths, understood as the height of a real vocal tract, were taken from X-Ray pictures, and they were averaged from the vocal tracts of a few people, who uttered the same vowels. The paper bases on Polish vowels: a,e,o,u,i,y.
7
Content available Speech nonfluency detection and classification based on linear prediction coefficients and neural networks
Kobus A., Kuniszyk-Jóźkowiak W., Smołka E., Codello I.
Journal of Medical Informatics & Technologies
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2010
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Vol. 15
135--143
EN
The goal of the paper is to present a speech nonfluency detection method based on linear prediction coefficients obtained by using the covariance method. The application “Dabar” was created for research. It implements three different methods of LP with the ability to send coefficients computed by them into the input of Kohonen networks. Neural networks were used to classify utterances in categories of fluent and nonfluent. The first one was Kohonen network (SOM), used to reduce LP coefficients representation of each window, which were used as input data to SOM input layer, to a vector of winning neurons of SOM output layer. Radial Basis Function (RBF) networks, linear networks and Multi-Layer Perceptrons were used as classifiers. The research was based on 55 fluent samples and 54 samples with blockades on plosives (p, b, d, t, k, g). The examination was finished with the outcome of 76% classifying.
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