@TECHREPORT{LehJoh06ParticleTR,
  author =      {Eric A. Lehmann and Anders M. Johansson},
  title =       {Particle Filter with Integrated Voice Activity Detection for Acoustic Source Tracking},
  type =        {{NICTA/WATRI} Technical Report},
  number =      {PRJ-NICTA-PM-008},
  institution = {Western Australian Telecommunications Research Institute},
  address =     {Perth, Australia},
  month =       {December},
  year =        {2006},
  abstract =    {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. In this report, 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 are presented on the basis of synthetic audio samples generated with the image method, furthermore performance results obtained with a real-time implementation of the algorithm, and using real audio data recorded in a reverberant room, are presented. 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.}
}