The problem of single-source localization with ad hoc microphone networks in noisy and reverberant enclosures is addressed in this paper. A training set is formed by prerecorded measurements collected in advance and consists of a limited number of labelled measurements, attached with corresponding positions, and a larger number of unlabelled measurements from unknown locations. Further information about the enclosure characteristics or the microphone positions is not required. We propose a Bayesian inference approach for estimating a function that maps measurement-based features to the corresponding positions. The signals measured by the microphones represent different viewpoints, which are combined in a unified statistical framework. For this purpose, the mapping function is modelled by a Gaussian process with a covariance function that encapsulates both the connections between pairs of microphones and the relations among the samples in the training set. The parameters of the process are estimated by optimizing a maximum likelihood criterion. In addition, a recursive adaptation mechanism is derived, where the new streaming measurements are used to update the model. Performance is demonstrated for both simulated data and real-life recordings in a variety of reverberation and noise levels.

Original languageEnglish
Pages (from-to)1477-1491
Number of pages15
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Issue number7
StatePublished - Jul 2017


  • Acoustic manifold
  • Gaussian process
  • maximum likelihood (ML)
  • relative transfer function (RTF)
  • sound source localization

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Acoustics and Ultrasonics
  • Computational Mathematics
  • Electrical and Electronic Engineering


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