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An Alternative Model for Sound Signals Encountered in Reverberant Environments; Robust Maximum Likelihood Localization and Parameter Estimation Based on a Sub-Gaussian Model

In this paper we investigate an alternative to the Gaussian density for modeling signals encountered in audio environments. The observation that sound signals are impulsive in nature, combined with the reverberation effects commonly encountered in audio, motivates the use of the Sub-Gaussian density. The new Sub-Gaussian statistical model and the separable solution of its Maximum Likelihood estimator are derived. These are used in an array scenario to demonstrate with both simulations and two different microphone arrays the achievable performance gains. The simulations exhibit the robustness of the sub-Gaussian based method while the real world experiments reveal a signi_cant performance gain, supporting the claim that the sub-Gaussian model is better suited for sound signals.

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