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You searched for +publisher:"University of Auckland" +contributor:("Dr. Waleed Abdulla"). Showing records 1 – 3 of 3 total matches.

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University of Auckland

1. Zeng, Qingning. Speech enhancement using a small microphone array.

Degree: 2010, University of Auckland

Microphone array based speech enhancement has wide applications. However, a big array aperture may greatly limit its applications. The research for small microphone array based speech enhancement has great value. Yet it is a challenging objective. In this thesis, some algorithms and methods for small microphone array based speech enhancement are proposed first. Then two main algorithms that synthesize the proposed algorithms and methods are presented. Firstly, the Multichannel Crosstalk Resistant Adaptive Noise Cancellation (MCRANC) algorithm is proposed. The algorithm employs only two adaptive filters. It has good stability and low computational complexity. Secondly, three combined algorithms of MCRANC with other existing algorithms are presented. One combined algorithm is the cascade of MCRANC with improved spectrum subtraction. The second is the combination with delay and sum beamforming, and the third is the combination with Weiner post-filtering. These combined algorithms may achieve better results than any one algorithm alone. Thirdly, four improvements are made for MCRANC itself. One is to improve MCRANC with multichannel distorted signals filtering for its second-stage filter. Another improvement is to employ multiple sampling rates for the array signals. The third is to add fixed beamforming and use partial-channel processing in MCRANC. The fourth improvement is to introduce subband processing to MCRANC. Fourthly, two improved MGSC algorithms based on multichannel crosstalk resistant adaptive signal cancellation (MCRASC) are proposed. One is to use every channel of the array signal as the main channel signal and others as the referential signals for an ACRASC to get the noise estimations. The other is to obtain the noise estimations by establishing a shared distorted speech signal. It is proved that the essence of the two proposed improved MGSC algorithms is to extend the common blocking matrix to a time-variable vector blocking matrix. Finally, two hybrid algorithms that employ several of the above-mentioned algorithms and methods are presented. Both of them can be used for different environments and both are suitable for real-time implementation. For all of the algorithms, simulations and experiments are made to verify their effectiveness. Advisors/Committee Members: Dr. Waleed Abdulla.

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APA (6th Edition):

Zeng, Q. (2010). Speech enhancement using a small microphone array. (Doctoral Dissertation). University of Auckland. Retrieved from http://hdl.handle.net/2292/5690

Chicago Manual of Style (16th Edition):

Zeng, Qingning. “Speech enhancement using a small microphone array.” 2010. Doctoral Dissertation, University of Auckland. Accessed April 16, 2021. http://hdl.handle.net/2292/5690.

MLA Handbook (7th Edition):

Zeng, Qingning. “Speech enhancement using a small microphone array.” 2010. Web. 16 Apr 2021.

Vancouver:

Zeng Q. Speech enhancement using a small microphone array. [Internet] [Doctoral dissertation]. University of Auckland; 2010. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2292/5690.

Council of Science Editors:

Zeng Q. Speech enhancement using a small microphone array. [Doctoral Dissertation]. University of Auckland; 2010. Available from: http://hdl.handle.net/2292/5690


University of Auckland

2. Cheng, Octavian. Embedded speech recognition systems.

Degree: 2008, University of Auckland

Apart from recognition accuracy, decoding speed and vocabulary size, another point of consideration when developing a practical ASR application is the adaptability of the system. An ASR system is more useful if it can cope with changes that are introduced by users, for example, new words and new grammar rules. In addition, the system can also automatically update the underlying knowledge sources, such as language model probabilities, for better recognition accuracy. Since the knowledge sources need to be adaptable, it is in??exible to statically combine them. It is because on-line modi??cation becomes di??cult once all the knowledge sources have been combined into one static search space. The second objective of the thesis is to develop an algorithm which allows dynamic integration of knowledge sources during decoding. In this approach, each knowledge source is represented by a weighted ??nite state transducer (WFST). The knowledge source that is subject to adaptation is factorized from the entire search space. The adapted knowledge source is then combined with the others during decoding. In this thesis, we propose a generalized dynamic WFST composition algorithm, which avoids the creation of non- coaccessible paths, performs weight look-ahead and does not impose any constraints to the topology of the WFSTs. Experimental results on Wall Street Journal (WSJ1) 20k- word trigram task show that our proposed approach has a better word accuracy versus real-time factor characteristics than other dynamic composition approaches. Advisors/Committee Members: Dr. Waleed Abdulla, Prof. Zoran Salcic.

Subjects/Keywords: Speech Recognition; Embedded Systems

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Cheng, O. (2008). Embedded speech recognition systems. (Doctoral Dissertation). University of Auckland. Retrieved from http://hdl.handle.net/2292/3279

Chicago Manual of Style (16th Edition):

Cheng, Octavian. “Embedded speech recognition systems.” 2008. Doctoral Dissertation, University of Auckland. Accessed April 16, 2021. http://hdl.handle.net/2292/3279.

MLA Handbook (7th Edition):

Cheng, Octavian. “Embedded speech recognition systems.” 2008. Web. 16 Apr 2021.

Vancouver:

Cheng O. Embedded speech recognition systems. [Internet] [Doctoral dissertation]. University of Auckland; 2008. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2292/3279.

Council of Science Editors:

Cheng O. Embedded speech recognition systems. [Doctoral Dissertation]. University of Auckland; 2008. Available from: http://hdl.handle.net/2292/3279


University of Auckland

3. Wong, Lisa, 1968-. Quantitative continuity feature for preterm neonatal EEG signal analysis.

Degree: 2009, University of Auckland

Electroencephalography (EEG) is an electrical signal recorded from a person's scalp, and is used to monitor the neurological state of the patient. This thesis proposes a quantified continuity feature to aid preterm neonatal EEG analysis. The continuity of EEG signals for preterm infants refers to the variation of the EEG amplitude, and is affected by the conceptional age of the infants. Currently, the continuity of the signal is determined largely by visual examination of the raw EEG signal, or by using general guidelines on amplitude-integrated EEG (aEEG), which is a compressed plot of the estimated signal envelope. The proposed parametric feature embodies the statistical distribution parameters of the signal amplitudes. The signal is first segmented into pseudo-stationary segments using Generalized Likelihood Ratio (GLR). These segments are used to construct a vector of amplitude, the distribution of which can be modelled using a log-normal distribution. The mean and standard deviation of the log-normal distribution are used as the continuity feature. This feature is less prone to the effects of local transient activities than the aEEG. This investigation has demonstrated that the degree of continuity corresponds to the major axis of the feature distribution in the feature space, and the minor axis roughly corresponds to the age of the infants in healthy files. Principal component analysis was performed on the feature, with the first coefficient used as a continuity index and the second coefficient as a maturation index. In this research, classifiers were developed to use the continuity feature to produce a qualitative continuity label. It was found that using a linear discriminant analysis based classifier, labelled data can be used as training data to produce labels consistent across all recordings. It was also found that unsupervised classifiers can assist in identifying the intrinsic clusters occurring in the recordings. It was concluded that the proposed continuity feature can be used to aid further research in neonatal EEG analysis. Further work should focus on using the continuity information to provide a context for further feature extraction and analysis. Advisors/Committee Members: Dr. Waleed Abdulla, Dr. Mark Andrews, Dr. Terrie Inder.

Subjects/Keywords: Signal Processing; EEG

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wong, Lisa, 1. (2009). Quantitative continuity feature for preterm neonatal EEG signal analysis. (Doctoral Dissertation). University of Auckland. Retrieved from http://hdl.handle.net/2292/4532

Chicago Manual of Style (16th Edition):

Wong, Lisa, 1968-. “Quantitative continuity feature for preterm neonatal EEG signal analysis.” 2009. Doctoral Dissertation, University of Auckland. Accessed April 16, 2021. http://hdl.handle.net/2292/4532.

MLA Handbook (7th Edition):

Wong, Lisa, 1968-. “Quantitative continuity feature for preterm neonatal EEG signal analysis.” 2009. Web. 16 Apr 2021.

Vancouver:

Wong, Lisa 1. Quantitative continuity feature for preterm neonatal EEG signal analysis. [Internet] [Doctoral dissertation]. University of Auckland; 2009. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2292/4532.

Council of Science Editors:

Wong, Lisa 1. Quantitative continuity feature for preterm neonatal EEG signal analysis. [Doctoral Dissertation]. University of Auckland; 2009. Available from: http://hdl.handle.net/2292/4532

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