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This is the flow diagram of a hearing aid that is currently under inves8ga8on at Essex University. The purpose of this talk is to explain how the design is linked to research using computer models of the human auditory periphery. 2
Recent research has shown that the strength of response to acous8c s8mula8on is 8ghtly regulated at the periphery using a number of different mechanisms. First, the mechanical response of the basilar membrane is subject to instantaneous compression for part of its range. This compression is approximately 2 db per 10 db. Second, there are two feedback loops that limit the response. The acous8c reflex (that limits the response of the stapes) and the medial olivocochlear efferent system (that limits the response of the basilar membrane near threshold). We are only beginning to understand the implica8ons of these func8ons. However, it is clear that they are influencing the perceived loudness of sounds and it has also been suggested that they may contribute to our ability to hear out speech against compe8ng acous8c backgrounds. 3
We propose that hearing aid design should take these func8ons into account because the impaired ear may feature malfunc8on of these protec8ve mechanisms, The proposed benefits for the pa8ents are various. Restora8on of instantaneous compression should give protec8on from sudden loud sounds. Feedback regula8on should reduce discomfort and result in increased willingness to par8cipate in noisy social situa8ons. such as restaurants par8es and musical events. By mimicking the natural form of compression, a hearing aid should give improved clarity through less distor8on. Anddi8onally we might look for beper speech in noise performance. 4
Most computer models of the auditory periphery simulate the ascending pathway of the auditory system as a cascade of stages from the middle ear through to the auditory nerve response and on to the brainstem. 5
The instantaneous compression described earlier is a feature of the basilar membrane stage of the model. It has the immediate func8on of regula8ng the mechanical driving force applies to the stereocilia of the inner hair cells. It is not the kind of compression found in an automa8c gain control (AGC) device because it has no 8me constant. The compression here is more like the effect of driving a car into a pile of sand (immediate but not quite as final as hixng brick wall). We know that this kind of compression is defec8ve in some kinds of hearing impairment where it may result in recruitment an abnormal rate of increase in loudness as a func8on of signal level. 6
The acous8c reflex is driven by loud sounds. Broadband sounds are more effec8ve than pure tones in elici8ng the reflex. The level of a broadband sound required to drive the reflex is lower than commonly thought at around 65 db SPL and needs to be taken into account when designing aids to deal effec8vely with speech in frequently encountered soundscapes. Considerable apenua8on can be observed in the region between 80 and 100 db SPL. Many hearing impaired listeners have no recordable acous8c reflex; its restora8on by means of the hearing aid is therefore poten8ally valuable. 7
The strength of the effect is propor8onal to the intensity of the elici8ng sound. Some reports indicate that at low frequencies, the resul8ng apenua8on can be 1 db per db some8mes known as perfect regula8on. In this case, the size of the response stops increasing even though the acous8c s8mulus con8nues to rise in intensity. Low frequencies are more effec8ve drivers of the acous8c reflex but it is not yet clear exactly how the reflex influences the frequency response of the system whatever textbooks confidently say about an impedence change. 8
The term reflex implies a super fast response but measurements suggest that the speed of the acous8c reflex depends on the intensity of the s8mulus and can be rela8vely slow. Even at high intensity, the reflex takes 0.3 s to asymptote and much longer responses can be observed for weaker s8muli. 9
The MOC reflex works by modifying the response of outer hair cells that are mechanically linked to the basilar membrane. While these cells are normally iden8fied with a cochlear amplifier mechanism, it is more convenient in this context to think of them as a cochlear breaking system ; the stronger the MOC control signal, the smaller the mechanical response of the basilar membrane. 10
The MOC reflex also takes 8me to reach asymptote. This experiment measures the rate of fall in the 2f1 f2 distor8on product when the MOC system is s8mulated by contralateral noise. The effect is abolished when the olivocochlear bundle is cut. This slide emphasises the fact that MOC suppression of response is both ipsilateral and contralateral in nature. In other words the response of one ear is regulated by sounds in both ears combined. This may have improtant signal processing implica8ons. 11
The MOC suppression interacts with the instantaneous compression on the basilar membrane. Animal studies have shown that the effect of MOC suppression is to shif the input output func8on to higher levels. In other words the level at which the instantaneous compression begins to be applied is raised. This has the effect of maintaining a region of linear response close to threshold while increasing thresholds in response to background noise. 12
The feedback circuits greatly complicate the nature of the auditory system s response to sound, to fluctua8ons of sounds and foreground sounds in compe8ng backgrounds. Because the response is also highly nonlinear 13
The feedback circuits greatly complicate the nature of the auditory system s response to sound, to fluctua8ons of sounds and foreground sounds in compe8ng backgrounds. Because the response is also highly nonlinear, it is difficult to have an intui8ve no8on of how the normal auditory system responds in any given situa8on. It also makes it virtually impossible to an8cipate the consequences of specific hearing impairments. Fortunately it is possible to simulate the system numerically using computer models. In this example the Essex model is represented as a cascade of stages represen8ng the individual processes illustrated in the previous sides. 14
Because we are interested mainly in the model s response to speech, we need to couple the output of the model to an automa8c speech recogni8on system. The output of the model is a mul8 channel representa8on of auditory nerve spiking ac8vity. We link this to the HTK publicly available automa8c recogniser. This yields a measure of speech recogni8on in terms of the number of words correctly recognised. In this example, the recogniser performs moderately well in quiet but its performance degrades as noise is added. 15
In this experiment we have run the model twice; once with the MOC efferent switched off and once when it func8ons normally. The recogni8on rate for speech in noise improves with normal MOC func8on. 16
It is possible to get a visual impression of why the MOC efferent system improves the percep8on in noise. The top image shows the mul8 channel AN spiking response to the uperance eight one six. Low frequency channels are at the bopom of the image. The recogniser is able to use this image to iden8fy the digits correctly. The middle image shows what happens when background babble is added; the image becomes fogged with noise and recogni8on performance suffers as a consequence. The bopom image shows how MOC suppression cleans up the image by thresholding out the noise. Recogni8on rates, not surprisingly, improve as a result. 17
The hearing aid design in this slide tries to build all of these func8ons into the hearing aid. The most important component is the bandpass/compression/bandpass unit in each ver8cal pathway. These compress the signal in a biologically plausible way. The process is also moderated by the MOC suppression formed by a nega8ve feedback loop. This loop has the virtuous property of filtering out many of the distor8on products generated by the compression resul8ng in a smooth clear sound. The acous8c reflex is incorporated into the aid by regula8ng the output of the aid to prevent high levels of sound. 18
In prac8ce, this is a binaural aid and the MOC control signal is derived from the combined response at both ears. 19
One might regard the MOC as a standard hearing aid AGC by another name. To some extent this is true but there are important differences. Firstly, the 8me constant is quite long (between 100 and 400 ms). While a normal AGC serves to protect the listener from sudden sounds, the MOC is more concerned with regula8ng the response over longer 8me periods. Protec8on from sudden sounds is achieved in the new design by the instantaneous compression. Another difference concerns the effect of the MOC which is to shif thresholds in line with ambient noise rather than to limit output. The threshold is set on the assump8on that sensi8vity need never be beper than 10 db below current ambient noise. By shifing the threshold, the aid is able to maintain a linear response to sounds at or above background sound levels. 20
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Protec8on from intensely loud sounds implies that the sound in the ear canal must be lower than the actual sound. This level of control can only be achieved if the canal is occluded. Occlusion is a nega8ve feature in a hearing aid when ambient sound levels are low. We recommend that a closed ear fixng be used only when high sound levels are an8cipated for example an evening at a cocktail party. Work is needed to develop a plug that can be fiped as an op8onal extra for special occasions. 22
The sexng of the aid parameters can be adjusted in real 8me by remote control while the aid is in place in order to find the best sexngs. 23
We are currently tes8ng the aid using speech recep8on thresholds in quiet and in noise as well as discomfort thresholds. Comparisons are made with no aid and the users regular hearing aid. 24
Good recep8on thresholds can be achieved and the aid is able to remove discomfort from excess noise completely. Improvements to speech in noise compared to own aid are currently being evaluated. 25
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The aid can be fiped purely on the basis of the audiogram. However, we are exploring the possibility of using addi8onal measures of frequency selec8vity ( tuning ) and compression. Our reasoning is that a knowledge of the degree of compression, in par8cular, can be used to avoid duplica8on of func8on by the new design. If a listener has residual compression in a par8cular frequency region, liple will be gained by duplica8ng this in the aid. Aid parameters could, in principle, be set by es8ma8ng compression and making a logical adjustment. another approach is to tune a computer model to match the pa8ent and then use the model to op8mise the parameters. In this slide, the doped lines show compression, frequency selec8vity and absolute threshold measurements of a person with very good hearing. The red lines are the measurements from a computer model tuned to an approximately normal configura8on. 27
A model can be adjusted to simulate the impaired hearing of an individual pa8ent if a diagnosis can be made as to which part of the hearing chain is malfunc8oning for example, outer hair cell loss or endocochlear poten8al loss as in strial presbyacusis or dead regions. The complete clinical cycle is shown here; measurement, diagnosis, individualised model construc8on and op8misa8on of the hearing aid parameters for this par8cular pa8ent. 28
Here are examples of measurements taken from four different pa8ents. Top lef panel shows an example of a cookie bite impairment. Top right panel shows a high frequency loss BoPom lef panel shows a flat loss and BoPom right an example of complete loss of tuning. All profiles show a loss of compression compared with normal (top row of each pane, doped line is normal) but some are more severe than others. 29
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