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Underdetermined Blind Audio Signal Separation
Čermák, Jan ; Smékal, Zdeněk (advisor)
We often have to face the fact that several signals are mixed together in unknown environment. The signals must be first extracted from the mixture in order to interpret them correctly. This problem is in signal processing society called blind source separation. This dissertation thesis deals with multi-channel separation of audio signals in real environment, when the source signals outnumber the sensors. An introduction to blind source separation is presented in the first part of the thesis. The present state of separation methods is then analyzed. Based on this knowledge, the separation systems implementing fuzzy time-frequency mask are introduced. However these methods are still introducing nonlinear changes in the signal spectra, which can yield in musical noise. In order to reduce musical noise, novel methods combining time-frequency binary masking and beamforming are introduced. The new separation system performs linear spatial filtering even if the source signals outnumber the sensors. Finally, the separation systems are evaluated by objective and subjective tests in the last part of the thesis.
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Underdetermined Blind Audio Signal Separation
Čermák, Jan ; Smékal, Zdeněk (advisor)
We often have to face the fact that several signals are mixed together in unknown environment. The signals must be first extracted from the mixture in order to interpret them correctly. This problem is in signal processing society called blind source separation. This dissertation thesis deals with multi-channel separation of audio signals in real environment, when the source signals outnumber the sensors. An introduction to blind source separation is presented in the first part of the thesis. The present state of separation methods is then analyzed. Based on this knowledge, the separation systems implementing fuzzy time-frequency mask are introduced. However these methods are still introducing nonlinear changes in the signal spectra, which can yield in musical noise. In order to reduce musical noise, novel methods combining time-frequency binary masking and beamforming are introduced. The new separation system performs linear spatial filtering even if the source signals outnumber the sensors. Finally, the separation systems are evaluated by objective and subjective tests in the last part of the thesis.
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Experimental Comparison of Sparse Signal Recovery Algorithms
Hoskovec, J. ; Tichavský, Petr
This report presents an experimental comparison of some of the newest and/or most common algorithms that are used for solving the sparse recovery problem: matching pursuit, orthogonal matching pursuit (OMP), A*OMP, basis pursuit, re-weighted least square (also known as FOCUSS), re-weighted L1 optimization (RL1). The comparison is done on synthetic (random) data set (dictionary) of the size 50x250 and 500x2500.
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