Hearing impairments were suffered by 360 million people worldwide in 2012, comprising 328 million adults and 32 million children [1]. These numbers have been steadily increasing as the life expectancy increased with advancements in medicine and technology [2]. However, despite the overwhelming number of hearing-impaired people, market surveillance data show that only 1 in 5 adults have adopted hearing aids, and many hearing-impaired persons still hesitate to purchase hearing aids due to the high-cost and low satisfaction in various noisy situations [3].

When purchasing hearing aids, many people are unsatisfied with the settings prescribed by their audiologists because adjustments made in a quiet clinic environment are often not optimal for real-life use [4-6]. People new to hearing aids may not be aware of what an optimal setting is for them, which would affect the effectiveness of the initial adjustment at the hospital [7]. In such cases, the device user has no choice but to revisit the hospital to readjust the device settings as many times as needed until satisfied with the tuning. However, if there is no relevant hospital or tuning clinic near the user’s home or office, repeated visits can incur large time and financial burdens [8]. In addition, due to differences in personal preferences, a setting suitable for one environmental situation may not be suitable for others [5]. Some people prefer to have the directional microphone function enabled when listening to speech sounds in noisy environments, but disabled when listening to music in a music hall or speech sounds in a quiet room. Others prefer to leave the function enabled all the time, regardless of the ambient situation. Additionally, many prefer to adjust the level of noise suppression depending on the variations in the ambient noise, whereas others do not [4]. Further, it has been reported that audiologist-driven settings and patient-driven settings can be quite different [9,10]. For these reasons, a need exists for a hearing-support device that can auto-tune its internal parameters according to the user’s personal preferences under various ambient situations, and to meet this need, a trainable or self-fitting hearing aid was introduced [7].

In the initial stage of research on trainable hearing aids, the main focus of training was only the relationship between the intensity of ambient sounds (input sound level [ISL]) and the output volume of the device (amplification constant [AMP]) [7,9,11,12]. Later research revealed that many other factors besides the ISL affects human perception, such as listening situation (LS), type of ambient sound, and internal device factors such as the degree of noise suppression (DNS) [7]. For example, Chalupper et al. [4] demonstrated that the settings of a hearing aid, such as overall gain, compression parameters, noise-reduction strength, and activation/deactivation of the directional microphone, may be sensitive to personal preferences. However, even the recent studies on trainable hearing aids have reflected only one or at most two of these environmental factors into the device training [4,6,13,14], which limited the user satisfaction under various acoustic conditions. In other words, personal satisfaction of the user would be greatly improved if the device is able to reflect the owner’s personal preference in a more expanded environmental acoustic conditions, since the auditory perception is affected by surrounding complex acoustic conditions.

In this study, we propose a trainable hearing aid algorithm that reflects the user’s personal preferences in three environmental acoustic conditions (ISL, LS, and DNS), and evaluate the performance of the proposed algorithm using 10 normal-hearing subjects.

Table 1 provides the results of the ISL detection test and ISL interpolation test. The implemented ISL detector could detect the intensity of the actual input signals with a 0.2%–4.6% error rate, and in addition, it could also interpolate the volumes of the input signals as intended with a 0.3%–4.2% error rate.

Fig. 2 shows the AMP variation patterns when both training and verification were performed using TS_1. The AMPs in normal-operation mode followed the trained values in all situations with some amount of time delay, and parameter interpolation also operated properly when required. Errors between the trained and adjusted AMPs were 0.01–2.71 dB, 0–4.23 dB, and 0–3.33 dB for speech-only, noise-only, and speech-in-noise respectively.

Fig. 3 shows the AMP variation patterns when training was performed using TS_1 and verification using TS_2. The AMPs in normal-operation mode also followed the trained values in all situations, and interpolation operated properly. Errors between the trained and adjusted AMPs were 0.06–4.37 dB, 0.02–4.99 dB, and 0–11.20 dB for speech-only, noise-only, and speech-in-noise respectively.

Table 2 shows the user-selected AMP and β values during the training phase of the subjective test. RM-ANOVA results showed significant difference existed among the three listening situations for all ISL (in the order from 45 dB to 90 dB, F-value is 8.00, 15.81, 19.99, 14.75, 17.67, 11.58, 9.86, 9.06, 6.14, and 4.70 respectively and P<0.05), and further, post-hoc analysis indicated that, in speech-in-noise conditions, subjects select significantly higher AMP values compared to speech-only and noise-only in all ISL levels except 90 dB where this was the case only in noise-only (P=0.008, Bonferroni adjustment).

Tables 3 and 4 give the results of the CMOS test for T1–T3 and T2–T3, respectively. In both tables, higher score indicate preference of T3 setting over the other setting being compared to. The T3 setting showed higher CMOS scores than the T1 setting for all subjects and listening situations. In contrast, the T2 setting and the T3 setting showed almost equivalent CMOS scores; that is, the preferences were different among the subjects.

In the subjective test, all subjects showed significant differences in their AMP settings (P<0.05) in accordance with the variations in the LS condition under the same ISL (usually between noise-only and speech-in-noise). Also, the user-selected AMP values in speech-in-noise were generally higher than those in noise-only and speech-only situations under the same ISL, which implies that the listener prefers to reduce the volume of the device when there is no interesting sound (noise-only) or there is no acoustic interference (speech-only) to reduce the perceptual discomfort from the noise. In contrast, when an interesting sound is mixed with environmental interference, the listener usually prefers to increase the volume of the device to increase the intelligibility of the interesting sound while enduring the perceptual annoyance from the loud interference. This preference, though it was derived from normal-hearing subjects, demonstrates the high potential for LS classification to benefit hearing-impaired subjects. Furthermore, when considering the user-selected β values in speech-in-noise situations under different ISL condition, the participants all preferred different β settings (e.g., subjects 1, 2, 6, 8, 9, and 10 selected 2.65 and 3.47, subject 5 selected 1.76 and 2.65, and subjects 3, 4, and 7 selected 1.76, 2.65, and 3.47) with no apparent pattern. Additionally, participants sometimes preferred different β values as the ISL condition changed, which further demonstrates the necessity of user-adjustable noise-reduction algorithm (adjusting the DNS) for the trainable hearing aid. These results demonstrate that all ISL, LS, and DNS conditions can affect the results of training and as a result, affect the performance (personal satisfaction) of the device in real-life situations, which justifies the incorporation of all of those factors in the device operation.

In the CMOS test, the T3 setting showed significantly improved sound quality over the T1 setting [21]; however, the T3 setting showed no significant difference compared to the T2 setting despite the difference in parameter setting between T2 and T3 settings. These results may be associated with the fact that the LS classifier used in the current study occasionally misclassified noise-only (babble) situations as speech-only situations (Table 2), which may have introduced error to the results. In addition, the output of the LS classifier varied depending on when the user selection was made during the training phase (e.g., an identical babble noise could be classified as noise-only or speech-only, depending on the timing of the user selection), which greatly affected the classification accuracy among the participants. The CMOS test results between T2 and T3 would be improved by enhancing the performance of the LS classifier [5]. For example, looking at only the speech-only situation (which was least affected by the misclassification) in Table 4, T3 showed improved CMOS score (on average) in a majority of the subjects, which indicates the potential benefit of the proposed algorithm. In the current study, the proposed T3 setting resulted in CMOS scores 1.42, 0.60, and 0.51 (on average) higher than the T1 setting, in speech-only, noise-only, and speech-in-noise situations, respectively. The proposed T3 setting also resulted in scores 0.45 and 0.35 higher than the T2 setting, in speech-only and noise-only situations, respectively (scores were lower only in the speech-in-noise situation), which is consistent with the report by Chalupper et al. [4], which suggests that the classifier control training achieved more comfortable loudness ratings (in about 72%–88% of situations) than did the gain control training (in about 68%–82% of situations) on average.

There are three scenarios that may cause abnormal operation of the proposed algorithm in normal-operation mode. First, when the ambient sound level changes abruptly (by more than 5 dB), the output of the ISL detector may not detect the change as quickly because it uses the most recent 2.5 seconds of data to calculate (i.e., the input sound data from the previous track is temporarily dominant over the new one). As a result, the ISL detector may display a sound level with less than 5 dB difference (even though the change in environment was permanent, not temporary), and hence the algorithm assumes the environment did not change and proceed to implement incorrect internal parameters instead of refraining from an update for one interval as intended. For example, note the errors in the 1st recording of 65 dB shown in Fig. 2A, as well as the errors in the 1st recording of 65 dB and the 1st recording of 50 dB shown in Fig. 3A. Second, the LS classifier may misclassify the category of the input sounds during training or during normal-operation mode, which would negatively influence the database, thereby negatively affecting the auto-tuned internal parameters. For example, as shown in Figs. 2B, 3B, the 55 dB babble noise sound was misclassified as speech-only during the training phase (in other words, the parameters for 55 dB noise-only were recorded as 55 dB speech-only, which resulted in an absence of data for 55 dB in the noise-only database). As a result, the parameters for 55 dB babble noise was applied to 55 dB speech-only in normal-operation mode, and the parameters for the 55 dB babble noise-only situation were determined by interpolation of the surrounding values, 50 dB and 60 dB noise-only parameters. The errors in 55 dB (babble noise) and 50 dB (car noise) shown in Fig. 3B were also caused by this scenario. Third, the first and second error-inducing scenarios may occur simultaneously, as shown in Fig. 3C: the 1st recording of 80 dB (babble noise) and the 1st recording of 75 dB (car noise).

There are several limitations in the current study. First, during the subjective evaluations, we considered only one user-controllable noise reduction algorithm: β-adjustable beamforming. However, in commercial devices, additional algorithms may be required to achieve environment-adaptive adjustment, such as single-microphone-based noise-reduction algorithms using spectral subtraction and Wiener filtering [17,22]. To modify the currently implemented algorithm to have a more conventional hearing aid algorithm structure, it is necessary to include additional user-controllable algorithms, such as single-microphone-based noise-reduction, to better reflect users’ personal preferences for various degrees of ambient noise suppression and speech distortion. Second, wide dynamic-range compression was bypassed during the verification process because only normal-hearing subjects participated in the subjective test. However, in actual hearing aids, the use of wide dynamic-range compression is indispensable because it (with an additional fitting formula [23]) compensates for the threshold shift in the audiogram caused by damage to the inner and outer hair cells, which may also affect the personal parameter preferences set during the training phase [4,13]. For an implemented algorithm to be suitable for hearing-impaired persons, wide dynamic-range compression with appropriate band gains for individual hearing-impaired subjects should be added before the output volume adjustment, and then, the procedures described in the current study should be performed. In fact, compression learning achieved the largest comfortable loudness ratings, in about 76%–91% of situations, in the report of Chalupper et al. [4]. Third, the amount of time spent in the training phase was relatively short; continuous training of weeks or months after the initial training would be ideal to more accurately verify the effect of user training [24]. In addition, according to previous reports, the final parameter settings can be affected by the starting point of the training [22]. The effect of such initial parameter settings on the proposed algorithm should also be investigated in future works. Furthermore, in the current study, each subject performed the training phase with a one-signal-for-each-condition protocol. In real circumstances, the personal preferences in a specific condition can differ from time to time depending on the variations in emotional and circumstantial states of the device user. To deal with such ambiguous situations, in the current study, the algorithm was designed to override the previous parameter settings with the most recent parameter setting for the same exact environment. We based this on the assumption that the device user would adapt and become more familiar with the device, and as this happens, preference settings would also adapt. However, this overriding protocol may not always be appropriate in all situations; therefore, for example, it may be more effective to save all duplicated user selections for the same condition in the database, and then determine the output parameters by one of the following methods: (1) averaging the saved values or (2) giving different weights to the saved values in accordance with the recorded time, and averaging the weighted values. These approaches should also be considered in future studies. Finally, due to limited number of hearing-impaired patients available to participate in this study, normal-hearing persons participated in this study instead, which is the limitation of this study. Though this is less than ideal, the positive results observed with normal-hearing participants are highly likely to affect hearing-impaired persons in a similar way.

In conclusion, as the experimental results showed, the preferred settings for each of the ISL, LS, and DNS conditions were different among subjects, which justifies the incorporation of more environmental factors during the training phase for more dedicated device individualization. Based on the result of this study, it is very likely for greater personal satisfaction to be achieved under various ambient situations by reflecting individual preference to a more extensive acoustic conditions to further personalize hearing aid control.