Particle Filter Design Using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environmen
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Particle Filter Design Using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environments Eric A. Lehmann1 and Robert C. Williamson2, 3 1 Western
Australian Telecommunications Research Institute, 35 Stirling Highway, Crawley, WA 6009, Australia ICT Australia, Locked Bag 8001, Canberra, ACT 2601, Australia 3 Computer Science Laboratory, Australian National University, Canberra, ACT 0200, Australia 2 National
Received 23 January 2005; Revised 29 May 2005; Accepted 22 August 2005 Sequential Monte Carlo methods have been recently proposed to deal with the problem of acoustic source localisation and tracking using an array of microphones. Previous implementations make use of the basic bootstrap particle filter, whereas a more general approach involves the concept of importance sampling. In this paper, we develop a new particle filter for acoustic source localisation using importance sampling, and compare its tracking ability with that of a bootstrap algorithm proposed previously in the literature. Experimental results obtained with simulated reverberant samples and real audio recordings demonstrate that the new algorithm is more suitable for practical applications due to its reinitialisation capabilities, despite showing a slightly lower average tracking accuracy. A real-time implementation of the algorithm also shows that the proposed particle filter can reliably track a person talking in real reverberant rooms. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
The concept of acoustic source localisation and tracking (ASLT) plays an important role in many practical speech acquisition systems. Domains of application include teleconferencing, multimedia information processing, and handsfree telephony, to name but a few. Other applications, such as automatic speech recognition and speaker identification systems, are also very sensitive to the quality of the audio input signals. In most cases, exact knowledge of the speaker position is the key to acquiring clean speech using such tools as beamforming or equalisation principles. The multipath propagation of acoustic waves in practical environments, however, constitutes a major challenge to overcome for any tracking algorithm. Recently, methods based on a state-space approach (Bayesian filtering) have been developed to deal with this problem [1–3]. Because Bayesian filtering algorithms deliver location estimates based on a series of past measurements rather than the current observation only, these methods are more efficient at dealing with the spurious effects of acoustic reverberation than traditional ASLT algorithms. Also, a tracker based on state-space filtering involves a model of the specific target dynamics, providing information regarding how the source is more likely to evolve from one time step to the next. This enables the
tracker to effectively discriminate between observations originating from the true target and erroneous observations resulting from acoustic disturbances. Among the different met
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