The presence of acoustic noise in audio recordings is an ongoing issue that plagues many applications. This ambient background noise is difficult to reduce due to its unpredictable nature. Many single channel noise reduction techniques exist but are limited in that they may distort the desired speech signal due to overlapping spectral content of the speech and noise. It is therefore of interest to investigate the use of multichannel noise reduction algorithms to further attenuate noise while attempting to preserve the speech signal of interest. Specifically, this thesis looks to investigate the use of microphone arrays in conjunction with multichannel noise reduction algorithms to aid aiding in speaker identification. Recording a speaker in the presence of acoustic background noise ultimately limits the performance and confidence of speaker identification algorithms. In situations where it is impossible to control the noise environment where the speech sample is taken, noise reduction algorithms must be developed and applied to clean the speech signal in order to give speaker identification software a chance at a positive identification. Due to the limitations of single channel techniques, it is of interest to see if spatial information provided by microphone arrays can be exploited to aid in speaker identification. This thesis provides an exploration of several time domain multichannel noise reduction techniques including delay sum beamforming, multi-channel Wiener filtering, and Spatial-Temporal Prediction filtering. Each algorithm is prototyped and filter performance is evaluated using various simulations and experiments. A three-dimensional noise model is developed to simulate and compare the performance of the above methods and experimental results of three data collections are presented and analyzed. The algorithms are compared and recommendations are given for the use of each technique. Finally, ideas for future work are discussed to improve performance and implementation of these multichannel algorithms. Possible applications for this technology include audio surveillance, identity verification, video chatting, conference calling and sound source localization.

La Modélisation spatio-temporelle permet la prédiction d’une variable régionalisée à des sites non observés du domaine d’étude, basée sur l’observation de cette variable en quelques sites du domaine à différents temps t donnés. Dans cette thèse, l’approche que nous avons proposé consiste à coupler des modèles numériques et statistiques. En effet en privilégiant l’approche bayésienne nous avons combiné les différentes sources d’information : l’information spatiale apportée par les observations, l’information temporelle apportée par la boîte noire ainsi que l’information a priori connue du phénomène. Ce qui permet une meilleure prédiction et une bonne quantification de l’incertitude sur la prédiction. Nous avons aussi proposé un nouveau critère d’optimalité de plans d’expérience incorporant d’une part le contrôle de l’incertitude en chaque point du domaine et d’autre part la valeur espérée du phénomène. / Spatio-temporal modeling allows to make the prediction of a regionalized variable at unobserved points of a given field, based on the observations of this variable at some points of field at different times. In this thesis, we proposed a approach which combine numerical and statistical models. Indeed by using the Bayesian methods we combined the different sources of information : spatial information provided by the observations, temporal information provided by the black-box and the prior information on the phenomenon of interest. This approach allowed us to have a good prediction of the variable of interest and a good quantification of incertitude on this prediction. We also proposed a new method to construct experimental design by establishing a optimality criterion based on the uncertainty and the expected value of the phenomenon.