How To Get A Clean Frequency Response Experimentally

• Journal List
• Trends Amplif
• v.15(ane-two); 2011 Mar
• PMC4040834

Trends Amplif. 2011 Mar; 15(1-2): 77–83.

A Method to Remove Differences in Frequency Response Betwixt Commercial Hearing Aids to Allow Direct Comparison of the Sound Quality of Hearing-Aid Features

Rolph Houben

¹Academic Medical Heart Amsterdam, Clinical and Experimental Audiology, Amsterdam, The netherlands

Inge Brons

¹Academic Medical Center Amsterdam, Clinical and Experimental Audiology, Amsterdam, Kingdom of the netherlands

Wouter A. Dreschler

^aneAcademic Medical Center Amsterdam, Clinical and Experimental Audiology, Amsterdam, Kingdom of the netherlands

Abstruse

Goal: We want to remove differences in frequency response between dissimilar commercial hearing aids so that we can compare the sound quality of signal processing features from dissimilar hearing-aid in a future paired-comparing set-up. More specifically, we want to control for the confounding effects of the linear hearing aid response when evaluating nonlinear processing. This article presents a control procedure and evaluates its effectiveness. Method: We increased the similarity of hearing-aid recordings in three steps and used both an objective quality metric and listening tests to investigate if the recordings from different hearing aids were perceptually similar. Results: Neither was it sufficient to manually adjust the hearing-help insertion gain, nor was it sufficient to add together an additional bandwidth limitation to the recordings. Only afterward the application of an inverse filter the perceptual differences betwixt recordings were removed fairly. Conclusion: Information technology was possible to level the footing between unlike hearing devices, so to speak. This will permit future research to evaluate the sound quality of nonlinear signal processing features.

Keywords: hearing aids, perceptual evaluation, sound quality, noise reduction

Introduction

Most hearing aids currently marketed have advanced betoken processing schemes implemented, such equally noise reduction. In our experience, many clinicians do not actively select such techniques or their fitting options to meet the requirements of an private hearing-impaired listener. One reason for this is a lack of cognition about the processing details and their perceptual effects for the user. For instance, near research into noise reduction in (commercial) hearing aids was done by comparing different settings within the same hearing help (eastward.m., Bentler, 2005; Boymans & Dreschler, 2000; Mueller, Weber, & Hornsby, 2006). However, a clinician needs to exist able to choose as well between devices. Unfortunately, direct perceptual comparisons of the sound quality between unlike devices are uninformative because the perceptual effects are largely determined by other parameters not related to the signal processing under investigation. For case, the frequency dependent hearing-help gain can differ essentially across hearing aids, even for hearing aids fitted to the same hearing loss (eastward.g., Mueller, Bentler, & Wu, 2008, showed differences up to 15 dB). At that place tin also exist big differences in sound quality. Legarth, Simonsen, Bramsløw, Le Ray, and Zacharov (2010) fitted 4 hearing aids according to the aforementioned plumbing fixtures rule and found that for normal hearing listeners these four aids differed markedly in subjective audio quality (ranging from between "poor" and "fair" to "good" on a mean opinion scale). These examples illustrate clearly that aural differences between hearing aids cannot be removed merely by fitting them to the same hearing loss. Spectral characteristics tin strongly influence a sound-quality percept (Davis & Davidson, 1996; Gabrielsson, Schenkman, & Hagerman, 1988). For instance, Gabrielsson and Sjögren (1979) did an experiment in which participants had to describe the sound of eight different headphones. They found that the headphone with a 10-dB tiptop in the frequency response at iii kHz scored strongly on adjectives related to "sharp/hard/loud" and on adjectives related to "disturbance." In general, smoother frequency responses lead to meliorate sound-quality judgments (Arehart, Kates, & Anderson, 2010) and can improve the threshold of discomfort (Warner & Bentler, 2002).

In conclusion, there is need for a method that allows for perceptual comparison between features of hearing aids by removing the (usually big) differences in frequency response between devices. In this article, we will therefore answer the post-obit research question:

• Research Question: Is it possible to reduce the perceptual differences (without reducing sound quality) between a set of hearing-aid recordings so that the recordings are indistinguishable from each other, with the following three successive steps:

  1. Careful transmission aligning of the insertion gain of the hearing aids;

  2. Limitation of bandwidth of hearing aid recordings;

  3. Application of an inverse filter on the bandwidth limited hearing-aid recordings?

To respond this, we recorded the output of a selection of hearing aids and these recordings were processed in three varying degrees (careful adjustment of the insertion gain, adjustment with bandwidth limitation, and inverse filtering with bandwidth limitation) to minimize differences between them. A sound-quality model was used to decide objective differences in quality betwixt the hearing aids in each fix. Additionally, we did two listening experiments with six normal-hearing participants. In the first experiment, the participants had to detect which sound sample differed from two other identical samples. The outcome was the percentage of times the participants could detect differences betwixt the hearing aids, within each prepare of stimuli. Finally, we did a paired comparing examination in which the participants had to indicate which sample they would adopt for long-time listening. This test was meant to measure the event of our processing on the sound quality of the recordings.

Method

Experimental Setup

All recordings and experimental validations were washed in a sound-treated double-walled booth (2.xx × two.53 × two.0 m). The recording system consisted of a B&M Caput and Torso Simulator (HATS Type 4128C) fitted with a custom fabricated tight-plumbing equipment ear mould without venting. Sound signals were generated and recorded monaurally at a 44100-Hz sample charge per unit with a resolution of 24 bits. The digital signals were converted to the counterpart domain with a RME Fireface 800 sound card, and were presented to the hearing assist via a Samsung Servo 120 amplifier connected to a Tannoy Reveal 6 near-field monitoring speaker that was placed at 62 cm in front of the recording microphone (on axis). All free-field hearing-assist input signals were corrected for the speaker response and all signals were presented inside the direct sound field to minimize the influence of room reflections.

The hearing aids used in this report were five frequently used BTE hearing aids from different brands (Oticon Vigo Pro, Phonak Exélia Yard, ReSound Azure AZ80-DVI, Starkey Destiny 1200,Widex Mind 440), randomly coded as HA1 through HA5. All signal-processing features (directionality, feedback command, noise reduction, compression, frequency transposition, and so on) were turned off.

Stimuli

We recorded the hearing-assistance output for speech (Versfeld, Daalder, Festen, & Houtgast, 2000) in speech babble (Luts et al., 2010). We used speech in noise considering (a) this is the target signal for most signal-processing features in hearing aids, and (b) the evaluation has to take into account possible remaining differences in both the target speech and the background noise. The signal to racket ratio was chosen to exist +10 dB because this is a relevant ratio for spoken language in noise experiments and it is high enough to permit perception of possible distortions and coloring to both speech and racket. Note that all hearing aids add dissonance to the point. In our selection of hearing aids, the specified equivalent noise input level was between twenty to thirty dB SPL, and this resulted in a noise level nigh 45 dB lower than our boilerplate speech level. This was causeless not to influence the quality of the recordings of our oral communication in oral communication-shaped babble noise (at + 10 dB), every bit the low-level noises volition be masked past the groundwork noise.

Three sets of stimuli were made to answer the three parts of the research question. Set 1 consisted of the unprocessed hearing-aid recordings that were fabricated after manual adjustment of the insertion gain (i.e., the deviation between aided and unaided response). Set 2 was based on the same recordings, but the signals were limited in bandwidth, and in Prepare three these bandwidth limited recordings were likewise filtered with an inverse filter to remove differences in frequency response.

Stimulus Prepare one: Adjustment of the insertion gain

During the hearing-aid plumbing fixtures, the insertion gain was measured in-situ with pink racket. To simulate a realistic condition, nosotros selected a conductive hearing loss of 30 dB at 500 Hz and xv dB at 2 kHz, that resulted in a NAL-RP prescription (Dillon, 2001) of nigh 10 dB insertion proceeds in the low and mid frequencies. More precisely, the target insertion gain was 4 dB between 100 Hz and 125 Hz; x dB between 125 Hz and 2 kHz; decreasing to 0 dB at 2 kHz, and information technology was 0 dB from 4 to 6 kHz. This frequency range (100 Hz to half dozen kHz) was inside the specified operational frequency range for all hearing aids except for HA5 (its specified operational low-cease frequency is 200 Hz, only the aid was verified to requite reliable output to at least every bit low as 100 Hz). Although the fittings were carefully adjusted to obtain the same insertion gain for all hearing aids, several peaks and valleys remained in the responses, making them different from each other and from a flat frequency response. These remaining differences in proceeds betwixt the devices tin can be seen from the top panel in Figure 1 and were smaller than 4.5 dB upwards to 2 kHz and smaller than 12 dB between 2 and vi kHz.

Narrowband analyses of the hearing-help output for an input of pink racket at 70 dB SPL. The tiptop panel shows the spectra of the raw recordings for the five hearing aids, the bottom panel shows the spectra for the recordings that were filtered with the inverse filter and bandwidth limited.

Stimulus Set 2: Bandwidth limited recordings

During the fitting, we selected a linear setting (no dynamic range pinch) with the devices' fitting software for input sound levels between 50 and 95 dB SPL and we verified the linearity of the gain by electro-acoustical measurements. For input levels below 55 dB SPL, the response of HA3 turned out to be compressed to a higher place 6 kHz. To remove this nonlinearity, we express the frequency range of all devices to v.viii kHz. Additionally, we used a high pass filter to remove frequencies lower than 100 Hz to limit the frequency response to those frequencies that are clinically relevant (100 Hz through 5.eight kHz). The ring limitation filters were designed with Matlab (office "ellip") and were elliptical low-pass and loftier-pass filters of the seventh order with a pass-ring ripple of 0.1 dB, a finish ring attenuation of >50 dB, and low and loftier frequency articulatio genus points at 100 Hz and 5800 Hz, respectively.

Stimulus Set 3: Fully filtered recordings

Inverse filters were designed to remove the remaining irregularities (after careful manual aligning and bandwidth limiting) in the frequency response. For each hearing aid, one filter was calculated. The goal of the filter was to remove perceptually disturbing effects such every bit sound coloration, and not to compensate for hearing-assistance processing delay and the phase response. Therefore, the required transfer office was determined with linear arrangement identification (Bendat & Piersol, 2010). Every bit our recordings are intended to be used for speech-in-noise measurements, it sufficed to estimate the transfer role past simply dividing the output spectrum past the input spectrum. The frequency response was measured with pink noise, because this resembles the speech spectrum as a first-order approximation. The required filter response of the changed filter was obtained by comparing the hearing-assist output to that of a measurement microphone (B&K 2260) at the location of the hearing-aid microphone. The coefficients of the inverse filter were calculated with the Matlab function "fir2." The constructed filter had 500 taps and was designed for noncausal awarding (Smith, 1997) to correct for grouping delay and stage distortion introduced by the filter. The maximally required correction (difference between highest unwanted acme and lowest unwanted valley) was 22 dB and the maximum slope was 50 dB/octave and occurred around four kHz. These requirements were met by the digital filter. The resulting time-domain impulse response was windowed with a Hamming window. Other windows (e.g., a Bartlett window) might exist more suitable if an authentic low-frequency response is important, but this was not necessary now since our signals were limited to frequencies higher up 100 Hz. Figure 1 shows the response to pinkish noise for each hearing aid prior and mail filtering (excluding the band limitation). As expected, the changed filter reduced the differences in frequency responses between hearing aids. To remove differences in bandwidth, all stimuli were bandwidth limited with the same filters every bit used on the previous set up of stimuli.

Evaluation Methods

To assess the homogenization of the recordings in the three stimulus sets, an objective quality metric was used and two listening tests were done.

Objective Evaluation

We calculated the objective Hearing-Aid Speech Quality Index (HASQI, Kates & Arehart, 2010) for all stimuli. This index estimates the quality of a target signal past comparison information technology to a reference betoken. HASQI provides two result indices, one for linear effects and ane for non-linear effects. The calculation for linear effects considers the change in the long-term spectral shape caused by the processing, while ignoring any changes to the indicate envelope modulation. The calculation of nonlinear effects, by contrast, considers the change in point envelope modulations caused by the processing, whereas ignoring any long-term spectral changes. This nonlinear measure out is sensitive to the effects of racket, baloney, and nonlinear indicate processing, and is expected to exist rather insensitive to our noncausal changed filtering. The reference indicate was the original unfiltered digital input point (i.e., the original speech-in-noise wave file that was not candy by the hearing aids). The reason for using speech in dissonance as reference is that nosotros desire to detect whatsoever differences caused by the filter, irrespective of whether the differences occur in the speech or in the dissonance. An additional calculation using the make clean speech signal as reference gave the aforementioned linear HASQI scores and lower non-linear HASQI scores (with an average of 0.19) due to the fact that now the racket is not part of the reference but considered a distortion. An important observation for the validity of our arroyo with speech in dissonance was that the ranking of the hearing aids was the aforementioned for clean and noisy speech as reference signal. The target signals consisted of the three sets of stimuli. The calculation was done on the aforementioned three sentences that were used in the subjective measurements (see adjacent section). Calculation with fifty sentences gave near identical results and will therefore not be shown.

Listening Test: Detection

To investigate whether listeners tin can distinguish between the hearing-aid recordings, we conducted a listening experiment with six normal-hearing (ANSI, 2004) participants. Although different from the target group, nosotros chose normal-hearing listeners considering they are assumingly better at detecting differences between stimuli than hearing-impaired listeners. Listeners with a sensorineural hearing deficit may be expected to have not only poorer hearing sensitivity, but also poorer suprathreshold processing like frequency resolution (Moore, 1996), and modulation detection (Grant, Summers, & Leek, 1998). If differences cannot be detected by normal-hearing participants, we can be quite confident that these differences will too exist unnoticeable for hearing-impaired listeners. Participants were presented with iii stimuli of which ii were from the same hearing aid (standard) and one was from another assistance (target). The participants' task was to select the hearing help recording that differed from the other two (i.e., an odd-ball epitome). To limit the duration of the experiment, just Set 2 (bandwidth limited) and Set3 (fully filtered) were included and Ready 1 (the raw recordings, based on a manually optimized insertion gain) was omitted. The stimulus duration was on average ii.7 s (i.e., ane judgement of 1.7 southward with a 0.5 s pb-in and a 0.five sec pb-out). The stimuli were presented diotically with Sennheiser HDA200 headphones at 70 dB SPL. All combinations of hearing aids and filter atmospheric condition were presented at random in 1 session. Standard and target were always from the same stimulus set (i.e., bandwidth express or fully filtered). Recordings from each hearing aid were used as target with standards of the recordings of all other hearing aids and vice versa. In total 20 distinct stimulus pairs were included (5 × 4, including AAB and BBA) and each stimulus pair was tested 3 times, leading to threescore trials per filter condition and thus 120 trials per participant. Directly after the participants had given their response, they received feedback on whether they had chosen the correct stimulus and if not, which ane they should take chosen.

Listening Examination: Preference Judgment

To decide if the inverse filtering influenced the sound quality of the signals, we also did a paired-comparison examination in which the same participants were asked to choose the audio sample they preferred. The participant'south job was to make a option based on the question: "Imagine that you will take to listen to these signals all mean solar day. Which sound would you adopt for prolonged listening?" The choice was betwixt the fully filtered stimulus (Set three) and its counterpart from the same hearing aid that was only bandwidth limited (Prepare 2). The stimuli were identical to those from the previous experiment (iii comparisons per hearing assist and 5 × 3 = 15 comparisons per participant).

Results

Objective Evaluation

The results of the calculations with the HASQI model are shown in Figure 2. The mean linear index of the unfiltered signal (Set 1) of the five hearing aids was 0.865 (with a range of 0.853 to 0.872). For the bandwidth limited signals (Fix 2), it was 0.863 (with a range of 0.849 to 0.871), and for the fully filtered signals (Set up 3), it was 0.945 (with a range of 0.941 to 0.947). Bandwidth limiting did not reduce the maximum difference between two hearing-aids signals (0.02, for both Sets ane and 2), merely applying the full filter reduced the maximum difference to 0.006. For the nonlinear index, the average indices were 0.752 (with a range of 0.697 to 0.798) for the unprocessed, 0.759 (with a range of 0.685 to 0.793) for the bandwidth express signals, and 0.790 (with a range of 0.731 to 0.814) for the fully filtered signals. Thus, bandwidth limitation increased the maximum difference in the nonlinear index betwixt ii hearing-aid stimuli from 0.10 (Set one) to 0.11 (Set 2) and additional application of the inverse filter reduced the maximum difference to 0.08 (Set 3).

Results of the HASQI objective-quality model for the iii stimulus sets.

Listening Tests

Detection Job

Effigy 3 shows the percentages of right detection averaged over all participants. The average detection score for the bandwidth express signals was 87% and for the fully filtered signals information technology was 39%. A 2-way analysis of variance with participant (6 levels) as random event and hearing help (5 levels) and stimulus fix (two levels) as fixed effects indicated that the primary effect of stimulus set up (fully filtered vs. bandwidth limited) was highly significant, F(1,20) = 90, p < .0005. The interaction between participant and filter blazon was significant every bit well, F(5,20) = 6, p < .005. The other main and interaction furnishings were statistically insignificant (p > .1). To determine if the detection charge per unit of any of the hearing-aid signals was higher than hazard (33%), 1-sided t-tests were used with Bonferroni correction. For the bandwidth limited ready, all results were significant (p ≤ .001). For the fully filtered stimuli, none of the results were significant (p > .thirteen). A one-sided t-test on the pooled data of this set showed that the detection of the group of hearing aids was slightly college than chance: 39% with p < .002 (for this no Bonferroni correction was required).

Per centum of times the participants selected the right stimulus as deviant from the other two. Signals were only compared to others of the grouping they belonged to, that is, bandwidth filtered only (open up circles), or fully filtered (bandwidth limited and inversely filtered, filled circles). Risk level was 33% and mistake bars denote 95% confidence intervals.

Preference Judgment

Five of the half-dozen participants preferred the fully filtered signals over the bandwidth limited signals in all (100%) of the audio samples, the sixth participant preferred the fully filtered signals in 73% of the sound samples.

Discussion

The results signal that to reduce the perceptual differences betwixt hearing-assist recordings,

1. it was not sufficient to carefully adjust the insertion proceeds of the hearing aids;

2. information technology was not sufficient to limit the bandwidth of the recordings to that of the smallest device;

3. it was sufficient to apply a hearing-aid specific inverse filter on the bandwidth limited recordings.

Objective Evaluation

For both Sets ane and 2, the difference in score between the hearing aids was larger than for Set 3 (0.02 compared to 0.006). This indicates that both transmission adjustment of insertion gain (Set ane) and bandwidth limitation (Set 2) were not sufficient to make the hearing-aid recordings undistinguishable from each other, and additional awarding of the inverse filter (Set up three) was required. Moreover, the linear HASQI score was improved by the inverse filtering, which suggests that the filter actually improved sound quality.

The range of scores for the nonlinear HASQI metric was similar for all three sets. Every bit expected, the bandwidth limitation and the inverse filters did not profoundly influence the nonlinear HASQI score. Therefore, these results indicate that the changed filters did non add nonlinear distortions (at least for those aspects for which HASQI nonlinear is sensitive). HA3 and HA4 had lower scores than the other aids, simply this does non necessarily mean that the sound quality of these hearing aids is lower. The lower scores for HA3 and HA4 point that these aids were perhaps not operating completely linear, although all nonlinear processing was switched off. Indeed, HA3 was shown to be compressive higher up six kHz (see Method section) and the nonlinear alphabetize increased after ring-pass limiting. The reason for this is that the bandwidth limiting removed those frequencies that roughshod outside the linear range of the hearing aid: HA3 was the aid that express the bandwidth in the high frequencies. The reason for the lower score for HA4 is unknown and falls across the telescopic of this article.

Listening Tests

The fact that the detection of the "oddball" was much poorer for the fully filtered signals than for the signals that were bandwidth limited only, indicates that the changed filtering clearly increased the similarity betwixt the hearing-aid signals. The detection for the inverse filter for each of the 5 hearing aids did non deviate significantly from adventure.

The result for the pooled data set was slightly, simply significantly, above chance (detection was 39%). The larger number of comparisons, coupled with the fact that a Bonferroni correction was non necessary here, gave larger statistical power. However, the influence of this detection rate on perceptual comparisons is expected to be just pocket-sized since one volition exist primarily interested in differences between unmarried pairs and thus accept access to only a smaller number of comparisons than was used for the pooled data ready. The college than chance detection rate was probably acquired by small residual differences in frequency response between hearing aids. These pocket-sized differences are unlikely to lead to differences in quality judgments.

There was a significant interaction between participant and filter type: the departure in detection charge per unit between the fully filtered and the bandwidth limited signals depended on the participant. The reason for this is that some participants performed worse at the detection of the bandwidth limited signals, whereas the detection of the fully filtered signals was effectually run a risk for all participants. The interaction thus reflects that participants differ in the bigotry of the bandwidth limited signals and not in the discrimination of the fully filtered signals. This interaction will therefore non be relevant for utilise of the inverse filter.

The 2nd listening experiment showed that all participants preferred the fully filtered signals over the bandwidth limited signals. This supports the results from the objective quality model and indicates that the filtering did not degrade the sound quality and in fact improved it for all hearing aids. This leads to two conclusions. Outset, the fact that the filter did not lower the quality shows that the filter did not add distortions while reducing the differences betwixt hearing aids. Second, it shows that the quality of the recordings could be easily improved past flattening the frequency response. This agrees with results from previous research that a smoother frequency response leads to better audio quality judgments (Arehart et al., 2010). It supports the implication of this study that quality judgment tests across hearing aids should not be based on raw recordings because this can mask the effect of the processing nether investigation, simply that additional filtering is required.

Application of the Inverse Filter

An inverse filter has been shown to be able to compensate for the response of the hearing aids included in this study. This compensation likewise works after an additional indicate processing characteristic is turned on. The filter does this signal processing (such as noise reduction) itself considering the filtering acts at the output of the hearing assist and just corrects for the characteristics that remain equal with or without the noise reduction. All the same, filters cannot transparently correct for compression. In case a noise reduction is implemented such that it depends on a pinch phase, 1 would need to investigate pinch and racket reduction in interaction. An changed filter is then nonetheless required to remove differences in frequency response between hearing aids. The intended apply of this inquiry is to facilitate inquiry into hearing aids. Awarding of the changed filter in a clinical setting, (e.g., to permit clients to directly compare the effect of noise reduction between different devices) is cumbersome as the technique requires a specific filter for each device.

The normal-hearing participants preferred the recordings with a flattened frequency response. Mayhap, this issue carries over to listeners with hearing loss, specially for participants with mild conductive loss. If this is true, ane might contemplate to add a simplified version of the inverse filter to a hearing aid.

Instead of focusing on group results, recently the individualization of noise reduction in hearing aids has gained attention. The few available studies (Houben, Dijkstra, & Dreschler, 2011; Zakis, Hau, & Blamey, 2009) are inconclusive. The current approach might stimulate inquiry that focuses on individuals rather than on the group they belong to.

Conclusion

We conclude that the perceptual differences between recordings of different linearly fitted hearing-aids tin can be removed by awarding of an inverse filter in combination with a ring-pass filter. Awarding of such a filter might even improve the sound quality of the recordings. Withal, the primary objective is to remove large differences in frequency response between hearing aids, thereby facilitating the comparing of more subtle differences between hearing aids due to nonlinear processing. One time an inverse filter is designed for a specific hearing aid, it can also be practical on recordings with (nonlinear) processing, such every bit noise reduction, turned on.

Acknowledgments

Approval by the Medical Ethical Committee of AMC was obtained on 24 April 2008.

Footnotes

Authors' Note: Parts of this work were presented at the Workshop on Speech in Noise: Intelligibility and Quality, Lyon, France, 2011 and at the 130th AES Convention, London, United kingdom of great britain and northern ireland, May 2011.

Declaration of Conflicting Interests: The writer(due south) declared no potential conflicts of interest with respect to the inquiry, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the post-obit financial support for the research, authorship, and/or publication of this article: This work was supported past grants from STW (Dutch Foundation for Science and Engineering science) for the HearClip project.

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4040834/

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