Maximum likelihood parameter estimation under impulsive conditions, a sub-Gaussian signal approach
Signal Processing - Fractional calculus applications in signals and systems
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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 presented. 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 significant performance gain, supporting the claim that the sub-Gaussian model is better suited for sound signals