SIRCE: A Sensor Image Based Room-Centered Equalization System for Hearing Aids

Abstract Reverberant spaces create major problems for hearing impaired listeners Reverberation reduces the sound quality and intelligibility of speech for such listeners especially if they are hearing aid HA users Moreover reverberation reduces the effectiveness of many otherwise useful signal processing methods such as speech enhancement algorithms because reverberation introduces additional virtual sources and background noise that increase the complexity of the acoustic signals such algorithms must grapple with We propose a novel method called SIRCE that equalizes that is dereverberates speech and other audio signals in complex real world acoustic environments containing multiple unknown acoustic sources SIRCE has been designed for compatibility with and integration into NIH s Open Speech Platform initiative SIRCE s design comprises three critical innovative features it is room centric sensor image based and listener aware Room centric means that important components of the system are embedded in the acoustic space itself rather than residing in the user s ear the hearing aid There are many advantages to placing sensors microphones and processing components in rooms rather than ears reduced cost increased processing power relaxed form factor constraints practical deployment of more than two microphones and easy sharing of processing power and computational results between users A room centric design makes particular sense for an equalization system because reverberation itself is room specific Sensor image based means that SIRCE calculates the acoustic image of each active source in each sensor Sensor image extraction SIX is our innovative contribution to the active field of blind source separation BSS Sensor image extraction determines what the response of each microphone would be to each source in isolation even when multiple sources are always active simultaneously SIX computes multiple independent images of each acoustic source one for each microphone whereas typical BSS algorithms only generate a single estimate for each source This is important because the most effective dereverberation methods are multi channel algorithms that require solo or source separated inputs from multiple microphones Listener aware means that our system employs the signals from the listener s HA internal microphones a listener specific acuity profile and the listener specified source of interest target to determine whether that target is acoustically audible to the listener whether other sources are acoustically audible and the optimal processing strategy and best sensor image to present to the listener When a target is far from the listener and close to a room microphone listening to the HA internal mic response is often not the optimal choice! In this Phase I project we propose to validate the sensor images SIRCE computes quantify its ability to equalize reverberant speech and estimate the overall improvement in speech intelligibility SIRCE delivers The SIRCE system will help hearing aid users understand speech better in complex reverberant spaces Project Narrative Hearing aid users find reverberant spaces such as many classrooms burdensome because speech is hard to understand in rooms with strong echoes We propose to improve hearing healthcare by developing a hearing aid compatible system that would determine the reverberation patterns of a particular room and deliver an enhanced rendition of speech sounds from which interfering echoes have been scrubbed This product would use multiple microphones permanently placed in a classroom office or other reverberant space to separate individual sound sources identify and remove echoes and upon request deliver an enhanced speech signal without obstructive reverberations to the hearing aids of listeners in the area