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4D time-frequency representation for binaural speech signal processing
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Paper Abstract

Hearing is the ability to detect and process auditory information produced by the vibrating hair cilia residing in the corti of the ears to the auditory cortex of the brain via the auditory nerve. The primary and secondary corti of the brain interact with one another to distinguish and correlate the received information by distinguishing the varying spectrum of arriving frequencies. Binaural hearing is nature's way of employing the power inherent in working in pairs to process information, enhance sound perception, and reduce undesired noise. One ear might play a prominent role in sound recognition, while the other reinforces their perceived mutual information. Developing binaural hearing aid devices can be crucial in emulating the working powers of two ears and may be a step closer to significantly alleviating hearing loss of the inner ear. This can be accomplished by combining current speech research to already existing technologies such as RF communication between PDAs and Bluetooth. Ear Level Instrument (ELI) developed by Micro-tech Hearing Instruments and Starkey Laboratories is a good example of a digital bi-directional signal communicating between a PDA/mobile phone and Bluetooth. The agreement and disagreement of arriving auditory information to the Bluetooth device can be classified as sound and noise, respectively. Finding common features of arriving sound using a four coordinate system for sound analysis (four dimensional time-frequency representation), noise can be greatly reduced and hearing aids would become more efficient. Techniques developed by Szu within an Artificial Neural Network (ANN), Blind Source Separation (BSS), Adaptive Wavelets Transform (AWT), and Independent Component Analysis (ICA) hold many possibilities to the improvement of acoustic segmentation of phoneme, all of which will be discussed in this paper. Transmitted and perceived acoustic speech signal will improve, as the binaural hearing aid will emulate two ears in sound localization, speech understanding in noisy environment, and loudness differentiation.

Paper Details

Date Published: 17 April 2006
PDF: 11 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470V (17 April 2006); doi: 10.1117/12.668690
Show Author Affiliations
Raed Mikhael, George Washington Univ. (United States)
Harold H. Szu, George Washington Univ. (United States)

Published in SPIE Proceedings Vol. 6247:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Harold H. Szu, Editor(s)

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