Full metadata record
|dc.contributor||Department of Applied Mathematics||en_US|
|dc.contributor.advisor||Yiu, Ka-fai Cedric (AMA)||-|
|dc.publisher||Hong Kong Polytechnic University||-|
|dc.rights||All rights reserved||en_US|
|dc.title||On optimization methods for speech signal processing||en_US|
|dcterms.abstract||This thesis is concerned with optimization used in speech signal processing, including filters design problem, equalizer, and beamformer design problems. All these three problems play an essential role in signal speech processing. Firstly, the minimax design of infinite impulse response(IIR) filter is considered. Partial fraction method is applied to decompose the transfer function into a sum of low order fractions; as a result, the minimax IIR filter design problem can be formulated with the stability condition as constraints. Then, among different possible decompositions of the transfer function, it can be proved that the second order decomposition can always achieve a good approximation to the optimal solution. Based on the second order decomposition formulation, several numerical experiments have been conducted, and better IIR filters can be designed using the proposed method compared with existing methods in the literature. Secondly, a fixed equalizer aiming at noise reduction is designed by optimizing objective measures. There are many objective quality measures in speech quality testing, and one of the most popular ones is PESQ, and another latest measure is STOI. In this part, both of these two measures are considered as optimization criteria when designing weights of equalizers. To enhance robustly, there is a pre-training part of this method, and the average weights of the training section achieve as the coefficients of the fixed equalizer. Thirdly, array gain optimization methods are considered in speech enhancement. Algorithms, LS, and SNR, are used to find optimal weights, and the improved performance using these two methods are presented. From the results, we could see that LS concentrate more on distortion control comparing with SNIR method, but SINR performs better in noise suppression than LS. Besides, in the experimental part, the results consist of two parts, including the real data recorded by sensors and the created signals calculated by a transfer function.||en_US|
|dcterms.extent||xvi, 55 pages : illustrations||en_US|
|dcterms.LCSH||Hong Kong Polytechnic University -- Dissertations||en_US|
|dcterms.LCSH||Speech processing systems||en_US|
|dcterms.LCSH||Signal processing -- Digital techniques||en_US|
Files in This Item:
|991022270854903411.pdf||For All Users||506.09 kB||Adobe PDF||View/Open|
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item: