Advanced adaptive signal processing techniques for low complexity speech enhancement applications.
Doctoral thesis, UCL (University College London).
This thesis research is focused on using subband and multi rate adaptive signal processing techniques in order to develop practical speech enhancement algorithms. This thesis comprises of research on three different speech enhancement applications. Firstly, design of a novel method for attenuation of a siren signal in an emergency telephony system (by use of single source siren noise reduction algorithms) is investigated. The proposed method is based on wavelet filter banks and series of adaptive notch filters in order to detect and attenuate the siren noise signal with minimal effect on quality of speech signal. Results of my testing show that this algorithm provides superior results in comparison to prior art solutions. Secondly, effect of time and frequency resolution of a filter bank used in a statistical single source noise reduction algorithm is investigated. Following this study, a novel method for improvement of time domain noise reduction algorithm is presented. The suggested method is based on detection of transient elements of speech signal followed by a time varying signal dependent filter bank. This structure provides a high time resolution at points of transient in a noisy speech signal hence temporal smearing of the processed signal is avoided. Additionally, this algorithm provides high frequency resolution at other times which results in a good performing noise reduction algorithm and benchmarking results against a prior art algorithm and a commercially available noise reduction solution show better performance of proposed algorithm. The time domain nature of algorithm provides a low processing delay algorithm that is suitable for applications with low latency requirement such as hearing aid devices. Thirdly, a low footprint delayless subband adaptive filtering algorithm for applications with low processing delay requirement such as echo cancellation (EC) in telephony networks is proposed. The suggested algorithm saves substantial memory and MIPS and provides significantly faster convergence rate in comparison with prior art algorithms. Finally, challenges and issues for implementation of real-time audio signal processing algorithms on DSP chipsets (especially low power DSPs) are briefly explained and some applications of research conducted in this thesis are presented.
|Title:||Advanced adaptive signal processing techniques for low complexity speech enhancement applications|
|Additional information:||Permission for digitisation not received|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Electronic and Electrical Engineering|
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