Speech quality might significantly deteriorate in presence of interference. Multi-microphone measurements can be utilized to enhance speech quality and intelligibility only if room acoustics is taken into consideration. The vital role of the acoustic transfer function (ATF) between the sources and the microphones is demonstrated in two important cases: the minimum variance distortionless response (MVDR) and the linearly constrained minimum variance (LCMV) beamformers. The LCMV deals with the more general case of multiple desired speakers. It is argued that the MVDR beamformer exhibits a tradeoff between the amount of speech dereverberation and noise reduction. The level of noise reduction, sacrificed when complete dereverberation is required, is shown to depend on the direct-to-reverberation ratio. When the reverberation level is tolerable, practical beamformers can be designed by substituting the ATFs with their corresponding relative transfer functions (RTFs). As no dereverberation is performed by these beamformers, a higher level of noise reduction can be achieved. In comparison with the ATFs, the RTFs exhibit shorter impulse responses. Moreover, since non-blind procedures can be adopted, accurate RTF estimates might be obtained. Three such RTF estimation methods are discussed. Finally, a comprehensive experimental study in real acoustical environments demonstrates the benefits of using the proposed beamformers.
Skip Nav Destination
Article navigation
April 2012
Meeting abstract. No PDF available.
April 01 2012
On the importance of room acoustics in multi-microphone speech enhancement
Sharon Gannot
Sharon Gannot
Bar-Ilan University, gannotsh@gmail.com
Search for other works by this author on:
J. Acoust. Soc. Am. 131, 3209 (2012)
Citation
Sharon Gannot; On the importance of room acoustics in multi-microphone speech enhancement. J. Acoust. Soc. Am. 1 April 2012; 131 (4_Supplement): 3209. https://doi.org/10.1121/1.4707965
Download citation file:
38
Views
Citing articles via
A survey of sound source localization with deep learning methods
Pierre-Amaury Grumiaux, Srđan Kitić, et al.
Using soundscape simulation to evaluate compositions for a public space sound installation
Valérian Fraisse, Nadine Schütz, et al.
Source and propagation modelling scenarios for environmental impact assessment: Model verification
Michael A. Ainslie, Robert M. Laws, et al.