NATURAL BALANCE IN LISTENING WITH HEARING INSTRUMENTS

One way to account for situational preferences in volume without requiring action on the part of the wearer is for the hearing instrument to do so automatically. The Environmental Optimizer in the ReSound Azure hearing instruments can be set by the fitter to adjust the overall volume of the hearing instruments in use depending on the type of listening environment. Thus the wishes of a wearer who would like the volume for listening to soft speech a little higher can be accommodated without him having to manipulate the volume control. Likewise, if he would like the volume to be lower in noisy situations, this can also be configured to be carried out automatically.

There is no need to make compromises on overall gain. In fact, the Environmental Optimizer does not even require that the device have a volume control, so this facility works for all ReSound Azure hearing instruments. The Aventa fitting software automatically applies volume adjustments in seven listening environments based on degree of hearing loss. These suggested values are derived from in-house clinical trials, and are presented in Table 1. The settings reflect group tendencies, and are therefore conservative.

The hearing care professional can utilize sliding controls in the software to take personal volume preferences into account, as illustrated in Figure 7. When the hearing aid is being worn, the volume change is affected within 1.5 seconds when a new listening environment is identified.

Figure 7: Sliders for seven listening environments can be adjusted to account for individual volume preferences.

Table 1: Environmental Optimizer settings are applied depending on hearing loss severity. The fitter can override these settings for the individual listener

ENVIROMENTAL CLASSIFICATION

The success of the Environmental Optimizer is dependent on the ability of the hearing instrument to accurately and consistently identify acoustic environments, and to characterize these environments in terms which will be meaningful for the wearer. For example, a classification scheme which distinguishes different types of music might be interesting, but not particularly helpful in automatically adjusting a hearing instrument for everyday situations.

The Environmental Classification scheme developed for the ReSound Azure hearing instruments considers acoustic factors which are of importance to all hearing instrument wearers: Is there sound above an ambient level?, Is there speech present?, Are there other background noises present?, and what are the levels of the sounds present? Conceptualized in Figure 8, this information is used to categorize sound inputs into seven listening environments.

Figure 8: The ReSound Azure’s Environmental Classifier emphasizes relevance for communication and comfort by categorizing the incoming sound in terms of the presence and levels of speech and noise.

Although the classification scheme illustrated in Figure 8 appears simple, it is deceptively so. The Environmental Classifier employs sophisticated speech and noise detection algorithms based on frequency content and spectral balance, as well as the temporal properties of the incoming sound to determine the nature of the acoustic surroundings. Furthermore, the classification does not occur on the basis of stringent predetermined criteria, but rather on the basis of probabilistic models, resulting in classification of listening environments which has shown greater consistency with listener perception.

Evidence for the superiority of this classifier method was gathered by Tchorz et al (2006), who compared the environmental classification behavior of two state-ofthe art hearing instruments. In this study, sound samples which were subjectively judged as being either “quiet”, “speech-in-noise”, or “noise” were presented in an anechoic chamber with the hearing instrument mounted on an anthropomorphic mannequin, and the output of the hearing instrument’s environmental classification monitored. Both classification systems identified quiet acoustic environments correctly 100% of the time. However, in more acoustically varying environments, the ReSound system showed a much higher degree of accuracy, as seen in Figure 9. Regardless of the type of environment, the ReSound classification method was highly consistent with the subjective classification of environments.

Figure 9: The ReSound Azure’s environmental classifier has been shown to identify acoustic environments with a high degree of accuracy for varying acoustic environments (figure based on data from Tchorz, 2006). For the comparative state-of-the-art system, only quiet environments were consistently identified accurately.

LEARNING ENVIROMANTAL PREFERENCES

As discussed previously, the Environmental Optimizer enables the fitter to personalize volume settings according to the acoustic environment for all ReSound Azure hearing instruments. As an additional feature, wearers of devices with volume controls can actually train their instruments to their preferred volume settings in their real-world surroundings. This has an obvious advantage for both the hearing instrument user as well as the hearing care professional, as it reduces the need for fine-tuning visits to reach the best Environmental Optimizer settings for the individual.

Through usage of the volume control, the hearing instrument gradually learns and applies the wearer’s preferred volume settings in their actual listening environments. As this process occurs, less need to manipulate the volume control is experienced by the wearer. Because the learning is ongoing, it continually takes into account changes in volume preferences or hearing status as reflected in the way the volume control is used. As schematized in Figure 10, the Environmental Optimizer carries personalization of the fitting from group-based trends to the most individualized level.

Figure 10: The Environmental Optimizer offers extended levels of personalization to the individual wearer.

Figure 11 illustrates how the volume setting could be trained for a particular listening situation. In this example, Mrs. K puts on her ReSound Azure hearing instrument in the morning in the quiet of her bedroom and then enters her living room, where her husband looks up from his paper and starts a conversation. Mrs. K turns her volume control up 4 dB. The environment identified by the classifier changes from “Quiet” to “Soft speech” when her husband begins talking.

If we assume that this scenario occurs four days in a row, Figure 11 shows that Mrs. K adjusts the volume wheel less each day as the instrument learns her preference in this situation. The volume change learned by the instrument is gradual, as can be seen by the smooth nature of the “Learned gain” curve in Figure 11. This smoothing is determined by a confidence parameter which is updated each time the volume control is adjusted by the user. If the user consistently alters the volume in the same way and in a particular acoustic environment, the confidence that this action truly reflects her intent and preference increases. This reflects a Bayesian approach to learning, as the confidence in the validity of the hypothesis - namely that the volume change is truly preferred by the wearer – is changed as new evidence emerges (Dijkstra et al, 2007; Ypma et al, 2006; de Vries et al, 2006).

Inspired by this system of inductive logic, the action of the Environmental Optimizer is actually configured and controlled by the wearer. As a result, the hearing instrument defers to and supports the superior ability of the individual’s central auditory system to manage sound rather than making decisions for the individual based on theoretical considerations.

Figure 11: For ReSound Azure hearing instruments with volume controls, the instrument gradually and continuously learns the environmentally dependent volume preferences of the wearer. This example illustrates how the instrument gradually learns a 4 dB volume increase in a specific listening situation.

When an ReSound Azure hearing instrument wearer returns for a follow-up visit, the net effect of Environmental Optimizer settings made by the fitter and the learned volume preferences configured by the wearer are indicated by the position of the sliding controls in the Environmental Optimizer screen (Figure 7). The learned values for each environment resulting from the wearer’s actions alone are displayed in the Onboard Analyzer screen.