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Automatic Classification of Digital Modulations
Kubánková, Anna ; Novotný, Vít (advisor)
This dissertation thesis deals with a new method for digital modulation recognition. The history and present state of the topic is summarized in the introduction. Present methods together with their characteristic properties are described. The recognition by means of artificial neural is presented in more detail. After setting the objective of the dissertation thesis, the digital modulations that were chosen for recognition are described theoretically. The modulations FSK, MSK, BPSK, QPSK, and QAM-16 are concerned. These modulations are mostly used in modern communication systems. The method designed is based on the analysis of module and phase spectrograms of the modulated signals. Their histograms are used for the examination of the spectrogram properties. They provide information on the count of carrier frequencies in the signal, which is used for the FSK and MSK recognition, and on the count of phase states on which the BPSK, QPSK, and QAM-16 are classified. The spectrograms in that the characteristic attributes of the modulations are visible are obtained with the segment length equal to the symbol length. It was found that it is possible to correctly recognize the modulation with the known symbol length at the signal-to-noise ratio at least 0 dB. That is why it is necessary to detect the symbol length prior to the spectrogram calculation. Four methods were designed for this purpose: autocorrelation function, cepstrum analysis, wavelet transform, and LPC coefficients. These methods were algorithmized and analyzed with signals disturbed by the white Gaussian noise, phase noise and with signals passed through a multipass fading channel. The method of detection by means of cepstrum analysis proved the most suitable and reliable. Finally the new method for digital modulation recognition was verified with signals passed through a channel with properties close to the real one.
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Very limited Vocabulary Speech Recognizer
Vystavěl, Kamil ; Míča, Ivan (referee) ; Sysel, Petr (advisor)
This bachelor thesis deals with the implementation of voice diagnostic method with limited number of recognized words in Matlab environment. Recognizer is designed for recognition of isolated words and is based on the dynamic programming method. This method is realized by the dynamic time warping algorithm (DTW). Features of the speech signal are calculated by methods of short-term analysis in time and frequency domain and by methods that are based on cepstral analysis and linear predictive analysis. The representation of the word, which is generated from its features, is suitable for quantifying the degree of similarity with the representation of another word. In order to achieve the highest degree of similarity, the dynamic time warping algorithm eliminates influence of fluctuation of the speech rate by non-linear normalization time axis of one of the compared words. The degree of the similarity of the two compared words is enumerated as the words’ distance. The representations of known words are stored in a word-book. The unknown word is compared with all words in the word-book and recognizer calculates distances between every known word and the unknown word. The unknown word is defined as identical with the known word that has the shortest distance to the unknown word. The successfulness depends mainly on the choice of the features.
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Comparison of methods for identification of rolling bearing failures
Kokeš, Miroslav ; Hnidka,, Jakub (referee) ; Klusáček, Stanislav (advisor)
The aim of this master thesis is the comparison of selected methods and parameters for roller bearings diagnostics. Selected statistical parameters are kurtosis, crest factor, and parameter K(t). The other selected methods are envelope analysis, cepstral analysis, and ACEP method. These methods are implemented in LabVIEW software and compared based on noise resistance, computation speed, and overall capability of identifying roller bearing faults.
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Detection of the voice fundamental frequency
Chloupek, Jiří ; Mekyska, Jiří (referee) ; Sysel, Petr (advisor)
This bachelor thesis deals with the detection of the pitch man. The frequency of the basic tone is one of the basic parameters of speech signal in the frequency domain. In this thesis we describe several methods for pitch detection and practical application of correlation method and cepstral analysis.
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Comparison of methods for identification of rolling bearing failures
Kokeš, Miroslav ; Hnidka,, Jakub (referee) ; Klusáček, Stanislav (advisor)
The aim of this master thesis is the comparison of selected methods and parameters for roller bearings diagnostics. Selected statistical parameters are kurtosis, crest factor, and parameter K(t). The other selected methods are envelope analysis, cepstral analysis, and ACEP method. These methods are implemented in LabVIEW software and compared based on noise resistance, computation speed, and overall capability of identifying roller bearing faults.
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Detection of the voice fundamental frequency
Chloupek, Jiří ; Mekyska, Jiří (referee) ; Sysel, Petr (advisor)
This bachelor thesis deals with the detection of the pitch man. The frequency of the basic tone is one of the basic parameters of speech signal in the frequency domain. In this thesis we describe several methods for pitch detection and practical application of correlation method and cepstral analysis.
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Automatic Classification of Digital Modulations
Kubánková, Anna ; Novotný, Vít (advisor)
This dissertation thesis deals with a new method for digital modulation recognition. The history and present state of the topic is summarized in the introduction. Present methods together with their characteristic properties are described. The recognition by means of artificial neural is presented in more detail. After setting the objective of the dissertation thesis, the digital modulations that were chosen for recognition are described theoretically. The modulations FSK, MSK, BPSK, QPSK, and QAM-16 are concerned. These modulations are mostly used in modern communication systems. The method designed is based on the analysis of module and phase spectrograms of the modulated signals. Their histograms are used for the examination of the spectrogram properties. They provide information on the count of carrier frequencies in the signal, which is used for the FSK and MSK recognition, and on the count of phase states on which the BPSK, QPSK, and QAM-16 are classified. The spectrograms in that the characteristic attributes of the modulations are visible are obtained with the segment length equal to the symbol length. It was found that it is possible to correctly recognize the modulation with the known symbol length at the signal-to-noise ratio at least 0 dB. That is why it is necessary to detect the symbol length prior to the spectrogram calculation. Four methods were designed for this purpose: autocorrelation function, cepstrum analysis, wavelet transform, and LPC coefficients. These methods were algorithmized and analyzed with signals disturbed by the white Gaussian noise, phase noise and with signals passed through a multipass fading channel. The method of detection by means of cepstrum analysis proved the most suitable and reliable. Finally the new method for digital modulation recognition was verified with signals passed through a channel with properties close to the real one.
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|
|
Very limited Vocabulary Speech Recognizer
Vystavěl, Kamil ; Míča, Ivan (referee) ; Sysel, Petr (advisor)
This bachelor thesis deals with the implementation of voice diagnostic method with limited number of recognized words in Matlab environment. Recognizer is designed for recognition of isolated words and is based on the dynamic programming method. This method is realized by the dynamic time warping algorithm (DTW). Features of the speech signal are calculated by methods of short-term analysis in time and frequency domain and by methods that are based on cepstral analysis and linear predictive analysis. The representation of the word, which is generated from its features, is suitable for quantifying the degree of similarity with the representation of another word. In order to achieve the highest degree of similarity, the dynamic time warping algorithm eliminates influence of fluctuation of the speech rate by non-linear normalization time axis of one of the compared words. The degree of the similarity of the two compared words is enumerated as the words’ distance. The representations of known words are stored in a word-book. The unknown word is compared with all words in the word-book and recognizer calculates distances between every known word and the unknown word. The unknown word is defined as identical with the known word that has the shortest distance to the unknown word. The successfulness depends mainly on the choice of the features.
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