Analyse multidimensionnelle à l'aide d'un nouveau modèle multicanal et d'un algorithme de projections successives. Application à l'analyse d'images

Nous présentons une nouvelle formulation du problème de la modélisation multicanale qui repose sur une description compacte, au moyen d'une représentation d'état. L'optimisation des paramètres du modèle porte sur ses propriétés fréquentielles. Par ailleurs, nous proposons un algorithme de projections successives qui permet d'améliorer les caractéristiques spectrales du signal multicanal. La convergence de cet algorithme est également étudiée. Finalement, nous montrons l'intérêt de cette méthode en compression d'images

On the use of a decimative spectral estimation method based on eigenanalysis and SVD for formant and bandwidth tracking of speech signals

In this paper, a Decimative Spectral estimation method based on Eigenanalysis and SVD (Singular Value Decomposition) is presented and applied to speech signals in order to estimate Formant/Bandwidth values. The underlying model decomposes a signal into complex damped sinusoids. The algorithm is applied not only on speech samples but on a small amount of the autocorrelation coefficients of a speech frame as well, for finer estimation. Correct estimation of Formant/Bandwidth values depend on the model order thus, the requested number of poles. Overall, experimentation results indicate that the proposed methodology successfully estimates formant trajectories and their respective bandwidths

Formant estimation of speech signals using subspace-based spectral analysis

The objective of this paper is to propose a signal processing scheme that employs subspace-based spectral analysis for the purpose of formant estimation of speech signals. Specifically, the scheme is based on decimative spectral estimation that uses Eigenanalysis and SVD (Singular Value Decomposition). The underlying model assumes a decomposition of the processed signal into complex damped sinusoids. In the case of formant tracking, the algorithm is applied on a small amount of the autocorrelation coefficients of a speech frame. The proposed scheme is evaluated on both artificial and real speech utterances from the TIMIT database. For the first case, comparative results to standard methods are provided which indicate that the proposed methodology successfully estimates formant trajectories