Joint Acoustic Source Location and Orientation Estimation Using Sequential Monte Carlo
Standard acoustic source localization algorithms attempt to estimate the instantaneous location of a source based only on current data from a microphone sensor array. This is done regardless of previous location estimates. However more recent Sequential Monte Carlo based approaches have instead posed the problem using an evolving state-space framework. In this paper we take this approach further by exploiting the directionality of human speech sources. This allows us to estimate the orientation of the source within the room. Finally combining previous source localization methods with this work we outline how both parameters - location and orientation - may be estimated jointly. Examples are given of performance in a typically reverberant real office environment for both a stationary and a moving source.