Methods for signals analysis have been developing more and
more in last few decades. By discovering new ones or by upgrading
the existing algorithms, results of signal processing have been
significantly improved, especially if the emphasis is on the process of
removing noise from the signal or signal parameterization. A number
of analog and digital filters are present in many scientific fields
nowadays, and a technique that is more and more in use is the one
based on wavelets. Wavelet decomposition to its detail and
approximation coefficients is the method that provides significant
results in removing noise from the signal as well as in signal
parameterization.
This thesis presents the results of applying wavelets for the
reduction of noise and its negative effects in synthetized and
measured room impulse responses. Analysis is done with signals in
full frequency range (broadband signals) and signals filtered in octave
and third-octave bands. Different sets of wavelet pa...rameters are
analyzed with the aim of finding the optimal set of parameters that
will give the best results. White and pink noises are used in the whole
research observing their influences on room impulse responses and
energy decay curves generated from these responses. Two used
measures that quantify the achieved results are the dynamic range
improvement of the decay curves and noise floor reduction. Other
standard methods for noise removal and reducing noise effects in
room impulse responses are used for the purpose of comparison with
results achieved with wavelets.
Besides the method of noise removal, wavelets have also
found their place in the knee detection in energy decay curves
obtained from room impulse responses. The obtained results are also
compared with the standard methods from literature.
An important task when it comes to newly manufactured
industrial products, such as direct current motors, is to find
procedures and algorithms that will separate non-faulty and faulty
motors. Method that can be used for this purpose is related to
application of wavelets to audio signals containing sound generated
by analyzed motors. The detail coefficients obtained by the wavelet
decomposition process representing wavelet-based audio features may
show the mentioned differences between motors at certain levels of
decomposition. The achieved results are confirmed by applying the
introduced numerical measure called feature difference of the detail
coefficients at certain levels.
