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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.
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