Reduction of spatial sampling requirement in sound-based synthesis

Cae Nguyen, Robert L. Morrison, Minh N. Do

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study the problem of synthesizing the sound field at arbitrary locations and times from the recordings of an array of audio sensors. Given prior estimates of the locations and frequencies of the sound sources, such as those obtained using adaptive source localization, we characterize the spatio-temporal support of the sound field spectrum. This characterization allows the spatial sampling requirements to be reduced in comparison to when no prior estimates of the sources are utilized. We derive an adaptive interpolation kernel, based on the estimated spectral support, to reconstruct the sound-field function using measurements from sensors on a coarse spatial-sampling grid. Simulation results demonstrate the gain achieved in reduced sampling requirements by using the proposed adaptive interpolation approach.

Original languageEnglish (US)
Title of host publication2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
Pages289-292
Number of pages4
DOIs
StatePublished - 2007
Event2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP - St. Thomas, Virgin Islands, U.S.
Duration: Dec 12 2007Dec 14 2007

Publication series

Name2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

Other

Other2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
Country/TerritoryVirgin Islands, U.S.
CitySt. Thomas
Period12/12/0712/14/07

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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