Particle Filter with Integrated Voice Activity Detection for Acoustic Source Tracking
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Research Article Particle Filter with Integrated Voice Activity Detection for Acoustic Source Tracking Eric A. Lehmann and Anders M. Johansson Western Australian Telecommunications Research Institute, 35 Stirling Highway, Perth, WA 6009, Australia Received 28 February 2006; Revised 1 August 2006; Accepted 26 August 2006 Recommended by Joe C. Chen In noisy and reverberant environments, the problem of acoustic source localisation and tracking (ASLT) using an array of microphones presents a number of challenging difficulties. One of the main issues when considering real-world situations involving human speakers is the temporally discontinuous nature of speech signals: the presence of silence gaps in the speech can easily misguide the tracking algorithm, even in practical environments with low to moderate noise and reverberation levels. A natural extension of currently available sound source tracking algorithms is the integration of a voice activity detection (VAD) scheme. We describe a new ASLT algorithm based on a particle filtering (PF) approach, where VAD measurements are fused within the statistical framework of the PF implementation. Tracking accuracy results for the proposed method is presented on the basis of synthetic audio samples generated with the image method, whereas performance results obtained with a real-time implementation of the algorithm, and using real audio data recorded in a reverberant room, are published elsewhere. Compared to a previously proposed PF algorithm, the experimental results demonstrate the improved robustness of the method described in this work when tracking sources emitting real-world speech signals, which typically involve significant silence gaps between utterances. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.
1.
INTRODUCTION
The concept of speaker localisation and tracking using an array of acoustic sensors has become an increasingly important field of research over the last few years [1–3]. Typical applications such as teleconferencing, automated multi-media capture, smart meeting rooms and lecture theatres, and so forth, are fast becoming an engineering reality. This in turn requires the development of increasingly sophisticated algorithms to deal efficiently with problems related to background noise and acoustic reverberation during the audio data acquisition process. A major part of the literature on the specific topic of acoustic source localisation and tracking (ASLT) typically focuses on implementations involving human speakers [1– 9]. One of the major difficulties in a practical implementation of ASLT for speech-based applications lies in the nonstationary character of typical speech signals, with potentially significant silence periods existing between separate utterances. During such silence gaps, currently available ASLT methods will usually keep updating the source location estimates as if the speaker was still active. The algorithm is therefore likely to momentarily lose track of the true source
position since the updates are then based sole
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