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

摘要

In this paper we investigate an alternative to the Gaussian density for modeling signals encountered in audioenvironments. The observation that sound signals are impulsive in nature, combined with the reverberatione?ects 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 estimatorare derived. These are used in an array scenario to demonstrate with both simulations and two di?erentmicrophone arrays the achievable performance gains.The simulations exhibit the robustness of the sub-Gaussian based method while the real world experimentsreveal a signiˉcant performance gain, supporting the claim that the sub-Gaussian model is better suited forsound signals.