Time-Frequency Analysis and Filtering based on the Short-Time Fourier Transform

The joint time-frequency (TF) domain provides a convenient platform for signal analysis by involving the dimension of time in the frequency representation of a signal. A straightforward way to acquire localized knowledge about the frequency content of the signal at different times is to perform the Fourier transform over short-time intervals rather than processing the whole signal at once. The resulting TF representation is the short-time Fourier transform (STFT), which remains to <br/>date the most widely used method for the analysis of signals whose spectral content varies with time. Recent application examples of the STFT and its variants – e.g. the squared magnitude of the STFT known as the spectrogram – include signal denoising, instantaneous frequency estimation, and speech recognition.<br/>In this thesis, we first address the main limitation of the trade-off between time and frequency resolution for the TF analysis by proposing a novel adaptation procedure which properly adjusts the size of the analysis window over time. Our proposed approach achieves a high resolution TF representation, and can compare favorably with alternative time-adaptive spectrograms as well as <br/>with advanced quadratic representations.Second, we propose a new scheme for the time-frequency adaptation of the STFT in order to automatically determine the size and the phase of the analysis window at each time and frequency <br/>instant. This way, we can further improve the resolution of the conventional as well as the time-adaptive spectrograms.<br/>Finally, we focus on denoising non-stationary signals in the STFT domain. We introduced an optimized TF mask in the STFT domain, which is based on the concept of the multi-window spectrogram. Experimentation has shown that the introduced approach can effectively recover distorted signals based on a small set of representative examples of the noisy observation and the desired signal.<br/>

Name dc:type.qualificationname

Doctor of Philosophy

Level dc:type.qualificationlevel

Doctoral Thesis

Grantor dc:publisher.institution

King's College London

Year dc:date.issued

2013

Author dc:creator

• Hon, Tsz Kin

Advisors dc:contributor.advisor

• Georgakis, Apostolos
• Cvetkovic, Zoran

Language dc:language

eng

Identifier

oai:kclpure.kcl.ac.uk:studenttheses/de8bcca8-cd9d-42a3-bf79-281672478744

OAI identifier oai:identifier

oai:kclpure.kcl.ac.uk:studenttheses/de8bcca8-cd9d-42a3-bf79-281672478744