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Month: December 2016

“Localization of moving microphone arrays from moving sound sources for robot audition”

IEEEXplore Access:

in Proc. European Signal Processing Conf. (EUSIPCO), Budapest, Hungary, Aug. 2016

Authors:

C. Evers , A. H. Moore, and P. A. Naylor

Abstract:

Acoustic Simultaneous Localization and Mapping (a-SLAM) jointly localizes the trajectory of a microphone array installed on a moving platform, whilst estimating the acoustic map of surrounding sound sources, such as human speakers. Whilst traditional approaches for SLAM in the vision and optical research literature rely on the assumption that the surrounding map features are static, in the acoustic case the positions of talkers are usually time-varying due to head rotations and body movements. This paper demonstrates that tracking of moving sources can be incorporated in a-SLAM by modelling the acoustic map as a Random Finite Set (RFS) of multiple sources and explicitly imposing models of the source dynamics. The proposed approach is verified and its performance evaluated for realistic simulated data.

“Speaker localization with moving microphone arrays”

IEEEXplore Access:

in Proc. European Signal Processing Conf. (EUSIPCO), Budapest, Hungary, Aug. 2016

Authors:

C. Evers , Y. Dorfan, S. Gannot, and P. A. Naylor

Abstract:

Speaker localization algorithms often assume static location for all sensors. This assumption simplifies the models used, since all acoustic transfer functions are linear time invariant. In many applications this assumption is not valid. In this paper we address the localization challenge with moving microphone arrays. We propose two algorithms to find the speaker position. The first approach is a batch algorithm based on the maximum likelihood criterion, optimized via expectation-maximization iterations. The second approach is a particle filter for sequential Bayesian estimation. The performance of both approaches is evaluated and compared for simulated reverberant audio data from a microphone array with two sensors.

“2D direction of arrival estimation of multiple moving sources using a spherical microphone array”

IEEEXplore Access:

in Proc. European Signal Processing Conf. (EUSIPCO), Budapest, Hungary, Aug. 2016

Authors:

A. H. Moore, C. Evers , and P. A. Naylor,

Abstract:

Direction of arrival estimation using a spherical microphone array is an important and growing research area. One promising algorithm is the recently proposed Subspace Pseudo-Intensity Vector method. In this contribution the Subspace Pseudo-Intensity Vector method is combined with a state-of-the-art method for robustly estimating the centres of mass in a 2D histogram based on matching pursuits. The performance of the improved Subspace Pseudo-Intensity Vector method is evaluated in the context of localising multiple moving sources where it is shown to outperform competing methods in terms of clutter rate and the number of missed detections whilst remaining comparable in terms of localisation accuracy.