Speech coding at medium bit rates using analysis by synthesis techniques

Speech coding at medium bit rates using analysis by synthesis technique

A code excited linear predictive coder: using a moments algorithm

A speech coding algorithm was developed which was based on a new method of selecting the excitation signal from a codebook of residual error sequences. The residual error sequences in the codebook were generated from 512 frames of real speech signals. L.P.C. inverse filtering was used to obtain the residual signal. Each residual error signal was assigned an index. The index was generated using a moments algorithm. These indices were stored on a Graded Binary Tree. A Binary Search was then used to select the correct index. The use of a Graded Binary Tree in the coding algorithm reduced the search time. The algorithm faithfully reproduced the original speech when the test residual error signal was chosen from the training data. When the test residual error signal was outside the training data, synthetic speech of a recognisable quality was produced. Finally, the fundamentals of speech coders are discussed in detail and various developments are suggested

Improved compactly computable objective measures for predicting the acceptiability of speech communications systems

Issued as Monthly status reports [1-7], and Final report, Project no. E-21-61

Novel multiscale methods for nonlinear speech analysis

Cette thèse présente une recherche exploratoire sur l'application du Formalisme Microcanonique Multiéchelles (FMM) à l'analyse de la parole. Dérivé de principes issus en physique statistique, le FMM permet une analyse géométrique précise de la dynamique non linéaire des signaux complexes. Il est fondé sur l'estimation des paramètres géométriques locaux (les exposants de singularité) qui quantifient le degré de prédictibilité à chaque point du signal. Si correctement définis est estimés, ils fournissent des informations précieuses sur la dynamique locale de signaux complexes. Nous démontrons le potentiel du FMM dans l'analyse de la parole en développant: un algorithme performant pour la segmentation phonétique, un nouveau codeur, un algorithme robuste pour la détection précise des instants de fermeture glottale, un algorithme rapide pour l analyse par prédiction linéaire parcimonieuse et une solution efficace pour l approximation multipulse du signal source d'excitation.This thesis presents an exploratory research on the application of a nonlinear multiscale formalism, called the Microcanonical Multiscale Formalism (the MMF), to the analysis of speech signals. Derived from principles in Statistical Physics, the MMF allows accurate analysis of the nonlinear dynamics of complex signals. It relies on the estimation of local geometrical parameters, the singularity exponents (SE), which quantify the degree of predictability at each point of the signal domain. When correctly defined and estimated, these exponents can provide valuable information about the local dynamics of complex signals and has been successfully used in many applications ranging from signal representation to inference and prediction.We show the relevance of the MMF to speech analysis and develop several applications to show the strength and potential of the formalism. Using the MMF, in this thesis we introduce: a novel and accurate text-independent phonetic segmentation algorithm, a novel waveform coder, a robust accurate algorithm for detection of the Glottal Closure Instants, a closed-form solution for the problem of sparse linear prediction analysis and finally, an efficient algorithm for estimation of the excitation source signal.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Compressive Sampling of Speech Signals

Compressive sampling is an evolving technique that promises to effectively recover a sparsesignal from far fewer measurements than its dimension. The compressive sampling theoryassures almost an exact recovery of a sparse signal if the signal is sensed randomly where thenumber of the measurements taken is proportional to the sparsity level and a log factor of thesignal dimension. Encouraged by this emerging technique, we study the application ofcompressive sampling to speech signals.The speech signal is very dense in its natural domain; however speech residuals obtainedfrom linear prediction analysis of speech are nearly sparse. We apply compressive sampling tospeech signals, not directly but on the speech residuals obtained by conventional and robustlinear prediction techniques. We use a random measurement matrix to acquire the data then use§¤-1 minimization algorithms to recover the data. The recovered residuals are then used tosynthesize the speech signal. It was found that the compressive sampling process successfullyrecovers speech recorded both in clean and noisy environments. We further show that the qualityof the speech resulting from the compressed sampling process can be considerably enhanced byspectrally shaping the error spectrum. The recovered speech quality is said to be of high qualitywith SNR up to 15 dB at a compression factor of 0.4

Forward sequential algorithms for best basis selection

Abstract Recently, the problem of signal representation in terms of basis vectors from a large, "overcomplete", spanning dictionary has been the focus of much research. Achieving a succinct, or "sparse", representation is known as the problem of best basis representation. We consider methods which seek to solve this problem by sequentially building up a basis set for the signal. Three distinct algorithm types have appeared in the literature which we term Basic Matching Pursuit (BMP), Order Recursive Matching Pursuit (ORMP) and Modified Matching Pursuit (MMP). The algorithms are first described and then their computation is closely examined. Modifications are made to each of the procedures which improve their computational efficiency. Each algorithm's complexity is considered in two contexts: one where the dictionary is variable (time dependent), and the other where the dictionary is fixed (time independent). Experimental results are presented which demonstrate that the ORMP method is the best procedure in terms of its ability to give the most compact signal representation, followed by MMP and then BMP which gives the poorest results. Finally, weighing the performance of each algorithm, its computational complexity and the type of dictionary available, we make recommendations as to which algorithms should be used for a given problem