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Impulse noise detection in audio signals
Hůla, Josef ; Ištvánek, Matěj (referee) ; Mokrý, Ondřej (advisor)
Study disserts known method of detecting impulsive noise in audiosignal. Differential, filtering, autoregressive and ARMA methods are discussed. First, each method is theoretically examined and the character of impulsive disturbances is presented. Later an~implementation of each method is presented and results of their performance is compared. In order to have comparable results, the methods are tested on synthetic impulses with known position and duration and also on recordings containing real impulsive noise.
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Comparison of methods for RGB spectrogram construction of DNA
Postránecká, Tereza ; Provazník, Ivo (referee) ; Kubicová, Vladimíra (advisor)
This thesis discusses about possibilities of construction colour DNA spectrograms and about patterns which can be detected there. Spectrograms as tools of spectral analysis give us a simultaneous view of the local frequency throughout the nucleotide sequence. They are suitable for gene identification or gene regions identification, determination of global character about whole chromosomes and also give us a chance for the discovery of yet unknown regions of potential significance. For purpose of this kind of DNA analysis is possible to use digital signal processing methods. We can apply them on only after conversion of DNA sequence to numerical representation. Selection of correct numerical representation affects how well will be reflected biological features in numerical record which we need for another use in digital signal analysis.
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Making up missing audio signal sections
Pospíšil, Jiří ; Rášo, Ondřej (referee) ; Mach, Václav (advisor)
The goal of this bachelor’s thesis is to get introduced with methods for reconstruction of missing samples in audio signal using periodicity-based interpolation and AR model based interpolation. Further it’s introducing us with Audio Inpainting method based on sparse representation. In practical part there are programmed three algorithms based on these interpolation methods and described an algorithm which is used in Audio Inpainting. These algorithms are compared with objective methods, SNR measurements depending on gap length and value of input parameter.
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Restoration of damaged audio signals using autoregressive models
Soboňa, Matúš ; Rajmic, Pavel (referee) ; Mokrý, Ondřej (advisor)
The bachelor thesis deals with the problem of restoring audio signals damaged by sample loss, using autoregressive models. The restoration itself is solved by W. Etter and A. Janssen's algorithms. These algorithms are implemented in MATLAB and tested on artificial signals aswell as on real recordings. Algorithms are then compared based on quality of restoration dependent on different parameters of signals.
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Impulse noise detection in audio signals
Hůla, Josef ; Ištvánek, Matěj (referee) ; Mokrý, Ondřej (advisor)
Study disserts known method of detecting impulsive noise in audiosignal. Differential, filtering, autoregressive and ARMA methods are discussed. First, each method is theoretically examined and the character of impulsive disturbances is presented. Later an~implementation of each method is presented and results of their performance is compared. In order to have comparable results, the methods are tested on synthetic impulses with known position and duration and also on recordings containing real impulsive noise.
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Algorithms for Detection and Correction of Local Degradations in Digital Audio Signals
Kúdela, Jakub ; Toropila, Daniel (advisor) ; Petříček, Martin (referee)
Title: Algorithms for Detection and Correction of Local Degradations in Digital Audio Signals Author: Jakub K'udela Author's e-mail address: jakub.kudela@gmail.com Department: Department of Theoretical Computer Science and Mathematical Logic Thesis Supervisor: Mgr. Daniel Toropila Supervisor's e-mail address: daniel.toropila@mff.cuni.cz Abstract: Local degradations in audio signal are discontinuities in their wave- forms. They are caused by the nature of the recording process, or by aging of or damage to the recording medium. In many cases these discontinuities are un- wanted while listening, and so there exists a number of methods, whose aim is to restore degraded recordings. In the introduction, this thesis informs the reader about selected algorithms for detection and correction of local degradations in digital audio signals. One of the discussed algorithms is a custom aplication of artificial neural networks to the given problem. The implementation of selected algorithms and experiments are both parts of the thesis. The goal of the exper- iments is to both objectively and subjectively compare the performances of the selected algorithms. The thesis proposes a method for the objective evaluation of the quality of detection and correction, which, as will be shown, largely cor- responds to the subjective...
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Product processes as a tool for financial analysis
Krejčí, Kateřina ; Zichová, Jitka (advisor) ; Hurt, Jan (referee)
This bachelor thesis discusses product processes as a tool for modeling financial time series. The thesis is divided into the theoretical and the practical part. Basic issues are summarized in the theoretical part. Properties of some moments and correlations are described and derived in this part, parameter estimates of a product process are derived subsequently. The practical part deals further with the parameter estimates. The quality of derived parameter estimates is verified in a simulation study in software Mathematica 9 and the proposed estimates are applied to real financial data. Powered by TCPDF (www.tcpdf.org)
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Restoration of damaged audio signals using autoregressive models
Soboňa, Matúš ; Rajmic, Pavel (referee) ; Mokrý, Ondřej (advisor)
The bachelor thesis deals with the problem of restoring audio signals damaged by sample loss, using autoregressive models. The restoration itself is solved by W. Etter and A. Janssen's algorithms. These algorithms are implemented in MATLAB and tested on artificial signals aswell as on real recordings. Algorithms are then compared based on quality of restoration dependent on different parameters of signals.
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Electricity Load Forecasting Using Auto-Regressive and Artificial Neural Network Model
Vyčítal, Václav
In this paper a short review of two forecasting models Autoregressive and Artificial neural network is presented. Both models were used to demonstrate its superior performance in load forecasting issues. In the third section the results of load forecasting experiment are given. For obtained forecasted results mean absolute percentage error for autoregressive model was 0.644 % and for artificial neural network model 2.31 %. In this paper error distribution for both models is also shown.
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Autoregressive models
Rathouský, Marek ; Zichová, Jitka (advisor) ; Prášková, Zuzana (referee)
The purpose of this thesis is to compare the classic autoregressive model of order 1 to integer autoregressive model of order 1. Considering the popularity of AR(1) model, only the basics are covered within this thesis. The main focus is on the INAR(1) model. Operator ◦ necessary for INAR(1) definition is intro- duced alongside with its properties with proof. All of the non-trivial properties of INAR(1) are followed by detailed proof, stationarity condition is also derived. Common estimation techniques are described for poisson INAR(1) model. This thesis also contains simulation study, which focuses on the rate of convergence of estimates of parameters. 1
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