Decimative Spectral Estimation with Unconstrained Model Order

This paper presents a new state-space method for spectral estimation that performs decimation by any factor, it makes use of the full set of data and brings further apart the poles under consideration, while imposing almost no constraints to the size of the Hankel matrix (model order), as decimation increases. It is compared against two previously proposed techniques for spectral estimation (along with derived decimative versions), that lie among the most promising methods in the field of spectroscopy, where accuracy of parameter estimation is of utmost importance. Moreover, it is compared against a state-of-the-art purely decimative method proposed in literature. Experiments performed on simulated NMR signals prove the new method to be more robust, especially for low signal-to-noise ratio

Compression d'images au moyen d'une modélisation par sinusoïdes exponentielles

Une méthode de modélisation d'images par sinusoïdes exponentielles est présentée. La séparabilité du problème est soulignée par la décomposition en valeurs singulières de l'image ce qui entraîne que les paramètres peuvent être déterminés à partir de deux signaux multicanal. Un schéma de compression d'images est élaboré comme une application possible de la modélisation

A Novel Iterative Signal Enhancement Algorithm for Noise Reduction in Speech

This paper presents an iterative signal enhancement algorithm for noise reduction in speech. The algorithm is based on a truncated singular value decomposition (SVD) procedure, which has already been used as a tool for signal enhancement [1][2]. Compared to the classical algorithms, the novel algorithm gives rise to comparable improvements in signal-to-noise ratio (SNR). Moreover the algorithm has an improved frequency selectivity for filtering out the noise and performs better with respect to the higher formants of the speech. It can also be extended easily to multiple channels. 1 INTRODUCTION In many speech communication applications, like audioconferencing and hands-free mobile telephony, the recorded and transmitted speech signals contain a considerable amount of acoustic background noise. This is mainly due to the fact that the speaker is located at a certain distance from the recording microphones. Background noise can stem from stationary noise sources, but most of the time th..