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.

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In this paper we propose an iterative audio restoration algorithm based on an autoregressive (AR) model with modeling of the noise pulse template to detect and restore Cell-phone electromagnetic interference (EMI) patterns known as “GSM buzz”. The algorithm is purely software based and does not require the aid of any hardware providing side information. The only assumption is that individual pulses are similar to scaled versions of the known template. With this assumption, the algorithm can fully detect and restore noisy interference signals in real time with almost no audible artifacts and improve the signal to noise ratio by as much as 50dB.

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