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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Advanced speech processing and coding techniques

Al-Naimi, Khaldoon Taha January 2002 (has links)
Over the past two decades there has been substantial growth in speech communications and new speech related applications. Bandwidth constraints led researchers to investigate ways of compressing speech signals whilst maintaining speech quality and intelligibility so as to increase the possible number of customers for the given bandwidth. Because of this a variety of speech coding techniques have been proposed over this period. At the heart of any proposed speech coding method is quantisation of the speech production model parameters that need to be transmitted to the decoder. Quantisation is a controlling factor for the targeted bit rates and for meeting quality requirements. The objectives of the research presented in this thesis are twofold. The first enabling the development of a very low bit rate speech coder which maintains quality and intelligibility. This includes increasing the robustness to various operating conditions as well as enhancing the estimation and improving the quantisation of speech model parameters. The second objective is to provide a method for enhancing the performance of an existing speech related application. The first objective is tackled with the aid of three techniques. Firstly, various novel estimation techniques are proposed which are such that the resultant estimated speech production model parameters have less redundant information and are highly correlated. This leads to easier quantisation (due to higher correlation) and therefore to bit saving. The second approach is to make use of the joint effect of the quantisation of spectral parameters (i.e. LSF and spectral amplitudes) for their big impact on the overall bit allocation required. Work towards the first objective also includes a third technique which enhances the estimation of a speech model parameter (i.e. the pitch) through a robust statistics-based post-processing (or tracking) method which operates in noise contaminated environments. Work towards the second objective focuses on an application where speech plays an important role, namely echo-canceller and noise-suppressor systems. A novel echo-canceller method is proposed which resolves most of the weaknesses present in existing echo-canceller systems and improves the system performance.
2

Robust speech filtering in impulsive noise environments

Ledoux, Christelle Michelle 31 December 1999 (has links)
This thesis presents a new robust filtering technique that suppresses impulsive noise in speech signals. The method makes use of Projection Statistics based on medians to detect segments of speech with impulses. The autoregressive model employed to smooth out the speech signal is identified by means of a robust nonlinear estimator known as the Schweppe-type Huber GM-estimator. Simulation results are presented that demonstrate the effectiveness of the filter. Another contribution of the work is the development of a robust version of the Kalman filter based on the Huber M-estimator. The performances of this filter are evaluated for a simple autoregressive process. / Master of Science
3

Melizmų sintezė dirbtinių neuronų tinklais / Melisma Synthesis Using Artificial Neural Networks

Leonavičius, Romas 12 January 2007 (has links)
Modern methods of speech synthesis are not suitable for restoration of song signals due to lack of vitality and intonation in the resulted sounds. The aim of presented work is to synthesize melismas met in Lithuanian folk songs, by applying Artificial Neural Networks. An analytical survey of rather a widespread literature is presented. First classification and comprehensive discussion of melismas are given. The theory of dynamic systems which will make the basis for studying melismas is presented and finally the relationship for modeling a melisma with nonlinear and dynamic systems is outlined. Investigation of the most widely used Linear Prediction Coding method and possibilities of its improvement. The modification of original Linear Prediction method based on dynamic LPC frame positioning is proposed. On its basis, the new melisma synthesis technique is presented. Developed flexible generalized melisma model, based on two Artificial Neural Networks – a Multilayer Perceptron and Adaline – as well as on two network training algorithms – Levenberg- Marquardt and the Least Squares error minimization – is presented. Moreover, original mathematical models of Fortis, Gruppett, Mordent and Trill are created, fit for synthesizing melismas, and their minimal sizes are proposed. The last chapter concerns experimental investigation, using over 500 melisma records, and corroborates application of the new mathematical models to melisma synthesis of one performer.
4

Melizmų sintezė dirbtinių neuronų tinklais / Melisma Synthesis Using Artificial Neural Networks

Leonavičius, Romas 12 January 2007 (has links)
Modern methods of speech synthesis are not suitable for restoration of song signals due to lack of vitality and intonation in the resulted sounds. The aim of presented work is to synthesize melismas met in Lithuanian folk songs, by applying Artificial Neural Networks. An analytical survey of rather a widespread literature is presented. First classification and comprehensive discussion of melismas are given. The theory of dynamic systems which will make the basis for studying melismas is presented and finally the relationship for modeling a melisma with nonlinear and dynamic systems is outlined. Investigation of the most widely used Linear Prediction Coding method and possibilities of its improvement. The modification of original Linear Prediction method based on dynamic LPC frame positioning is proposed. On its basis, the new melisma synthesis technique is presented. Developed flexible generalized melisma model, based on two Artificial Neural Networks – a Multilayer Perceptron and Adaline – as well as on two network training algorithms – Levenberg- Marquardt and the Least Squares error minimization – is presented. Moreover, original mathematical models of Fortis, Gruppett, Mordent and Trill are created, fit for synthesizing melismas, and their minimal sizes are proposed. The last chapter concerns experimental investigation, using over 500 melisma records, and corroborates application of the new mathematical models to melisma synthesis of one performer.

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