Robust signal processing techniques for source localization and multisource spatial sound rendering for immersive environments

In this thesis we investigate signal processing techniques that can find applications in acoustics. The methods under consideration can be separated in the application domain in: (i) robust source localization using microphone arrays and (ii) spatial sound source rendering; and in the theoretical domain as: (i) robust array signal processing for localization using (a) a low computational fractional lower order statistics method and (b) an optimal Maximum Likelihood localization for multiple dependent sources and noise, and (ii) modeling and filter reduction of the whole 3-D space using a state-space approach for spatial rendering.; Previous work in spatial sound reproduction using HRTF modeling has mainly focused on methods that attempt to model each transfer function individually. These methods are generally computationally-complex and cannot be used for real-time spatial rendering of multiple moving sources. We provide an alternative approach, which uses a multiple-input single-output state-space system to create a combined model of the HRTF's for all directions. This exploits the similarities among different HRTF's to achieve a significant reduction in model size with minimum loss of accuracy.; The robust array signal processing methods are based on the theory of alpha-stable distributions and two approaches are investigated. The first is a low computational alternative to second order statistics cross-correlation based methods. It is based on Fractional Lower-Order Statistics, which mitigate the effects of heavy-tailed noise. An improvement in TDE estimation is demonstrated that is up to a factor four better than what can be achieved with second-order statistics.; The second localization approach considers optimal, Maximum Likelihood localization for multiple sources that are dependent and are present in a dependent noise environment. This work aims to better model effects such as reverberation or echo that appear in room acoustics. We use Sub-Gaussian random processes, which are a special case of symmetric alpha-stable distributions. The Sub-Gaussian with alpha = 1—based on the Lévy (alpha = 0.5)—process is derived and used to formulate the ML function. The separable solution is derived for estimation of the statistics and the Directions-of-Arrival. Subsequently simulations are performed and verify the robustness claim of the Sub-Gaussian based method. Additionally two large linear microphone arrays (20 & 41 microphones) are created and show a significant improvement in localization with the proposed method.