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.
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members and is free for AES members and E-Library subscribers.