Neural minor component analysis approach to robust constrained beamforming
Neural minor component analysis approach to robust constrained beamforming
- Author(s): S. Fiori
- DOI: 10.1049/ip-vis:20030511
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- Author(s): S. Fiori 1
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Affiliations:
1: Faculty of Engineering, University of Perugia, Italy
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Affiliations:
1: Faculty of Engineering, University of Perugia, Italy
- Source:
Volume 150, Issue 4,
August 2003,
p.
205 – 218
DOI: 10.1049/ip-vis:20030511 , Print ISSN 1350-245X, Online ISSN 1359-7108
Since the pioneering work of Amari and Oja, principal component neural networks and their extensions have become an active adaptive signal processing research field. One of such extensions is minor component analysis (MCA), that proves to be effective in tasks such as robust curve/surface fitting and noise reduction. The aims of the paper are to give a detailed and homogeneous review of one-unit first minor/principal component analysis and to propose an application to robust constrained beamforming. In particular, after a careful presentation of first/minor component analysis algorithms based on a single adaptive neuron, along with relevant convergence/steady-state theorems, it is shown how the adaptive robust constrained beamforming theory by Cox et al. may be advantageously recast into an MCA setting. Experimental results obtained with a triangular array of microphones introduced in a teleconference context help to assess the usefulness of the proposed theory.
Inspec keywords: audio signal processing; neural nets; curve fitting; acoustic noise; principal component analysis; array signal processing; adaptive signal processing; surface fitting; random noise; microphones
Other keywords:
Subjects: Speech and audio signal processing; Interpolation and function approximation (numerical analysis); Other topics in statistics; Neural computing techniques; Interpolation and function approximation (numerical analysis); Signal processing theory; Other topics in statistics; Neural nets (theory)
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