Position and trajectory learning for microphone arrays

Paris Smaragdis, Petros Boufounos

Research output: Contribution to journalArticlepeer-review


In this paper, we tackle the problem of source localization by example. We present a methodology that allows a user to train a microphone array system using signals from a set of positions and trajectories and subsequently recall the localization information when presented with new input signals. To do so we present a new statistical model which is capable of accurately describing features from the cross spectra of the microphone signals so as to model the room responses from all positions of interest. We further extend this model to allow modeling of sequences of positions, thereby also enabling the learning and recognition of trajectories. Because of its learning nature this method provides practical advantages in setting up a microphone array, by not requiring favorable room acoustics, careful element positioning or uniformity of sensors. It also introduces an approach to localization which can be extended to other problems requiring models of transfer functions. We present tests on synthetic and real-world data and present the resulting recognition rates for a variety of situations.

Original languageEnglish (US)
Article number4032796
Pages (from-to)358-368
Number of pages11
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number1
StatePublished - Jan 1 2007
Externally publishedYes


  • Localization
  • Microphone arrays
  • Phase wrapping

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering


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