Acoustic parameter extraction from occupied rooms utilizing blind source separation

  • Authors:

  • Yonggang Zhang;Jonathon A. Chambers;Paul Kendrick;Trevor J. Cox;Francis F. Li

  • Affiliations:

  • The Centre of Digital Signal Processing, Cardiff School of Engineering, Cardiff University, Cardiff, UK;The Centre of Digital Signal Processing, Cardiff School of Engineering, Cardiff University, Cardiff, UK;School of Acoustics and Electronic Engineering, University of Salford, Salford, UK;School of Acoustics and Electronic Engineering, University of Salford, Salford, UK;Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK

  • Venue:
  • KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
  • Year:
  • 2006

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Abstract

Room acoustic parameters such as reverberation time (RT) can be extracted from passively received speech signals by some ‘blind' methods, which mitigates the need for good controlled excitation signals or prior information of the room geometry. However, noise will degrade such methods greatly. In this paper a new framework is proposed to extend these methods for room parameter extraction from noise-free cases to more realistic noise environment, such as occupied rooms, where noises are generated by occupants. In this proposed framework, blind source separation (BSS) is combined with an adaptive noise canceller (ANC) to remove the noise from the passively received reverberant speech signal. Room acoustic parameters can then be extracted from the output of the ANC with existing ‘blind' methods. As a demonstration we will utilize this framework combined with a maximum-likelihood (ML) based method to estimate the RT of a simulated occupied room. Simulation results show that the proposed framework provides a good estimate of the RT in such a simulated occupied room.