Algorithm lets users train aid to optimize compression, frequency shape, and gain

Chalupper, Josef; Junius, Dirk; Powers, Thomas A.

doi:10.1097/01.hj.0000359131.58356.8f

Cited by 13 publications

(28 citation statements)

References 9 publications

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“…Siemens SoundLearning was capable of training the gain-frequency response and compression characteristics by relating preferred gain settings to the input level in four frequency bands (Chalupper et al, 2009). SoundLearning 2.0 added learning in three different ESCs—Speech, Noise, and Music—as well as mixtures of these environments (Powers and Chalupper, 2010).…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based System for Learning and Inferring Hearing Aid Settings

Aldaz¹,

Puria²,

Leifer³

2016

J Am Acad Audiol

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Background Previous research has shown that hearing aid wearers can successfully self-train their instruments’ gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off). The baseline for comparison was the “untrained system,” that is, the manufacturer’s algorithm for automatically selecting microphone mode and noise reduction state based on acoustic environment. The “trained system” first learned each individual’s preferences, self-entered via a smartphone in real-world situations, to build a trained model. The system then predicted the optimal setting (among available choices) using an inference engine, which considered the trained model and current context (e.g., sound environment, location, and time). Purpose To develop a smartphone-based prototype hearing system that can be trained to learn preferred user settings. Determine whether user study participants showed a preference for trained over untrained system settings. Research Design An experimental within-participants study. Participants used a prototype hearing system—comprising two hearing aids, Android smartphone, and body-worn gateway device—for ~6 weeks. Study Sample Sixteen adults with mild-to-moderate sensorineural hearing loss (HL) (ten males, six females; mean age = 55.5 yr). Fifteen had ≥6 mo of experience wearing hearing aids, and 14 had previous experience using smartphones. Intervention Participants were fitted and instructed to perform daily comparisons of settings (“listening evaluations”) through a smartphone-based software application called Hearing Aid Learning and Inference Controller (HALIC). In the four-week-long training phase, HALIC recorded individual listening preferences along with sensor data from the smartphone—including environmental sound classification, sound level, and location—to build trained models. In the subsequent two-week-long validation phase, participants performed blinded listening evaluations comparing settings predicted by the trained system (“trained settings”) to those suggested by the hearing aids’ untrained system (“untrained settings”). Data Collection and Analysis We analyzed data collected on the smartphone and hearing aids during the study. We also obtained audiometric and demographic information. Results Overall, the 15 participants with valid data significantly preferred trained settings to untrained settings (paired-samples t test). Seven participants had a significant preference for trained settings, while one had a significant preference for untrained settings (binomial test). The remaining seven participants had nonsignificant preferences. Pooling data across participants...

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Section: Introductionmentioning

confidence: 99%

Smartphone-Based System for Learning and Inferring Hearing Aid Settings

Aldaz¹,

Puria²,

Leifer³

2016

J Am Acad Audiol

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“…Moreover, users who are satisfied with a general prescription model in a particular environment may not be as satisfied in other environments. Other settings of the hearing aid, such as overall gain, compression parameters, noise reduction strength and activation/deactivation of directional microphones, may also be sensitive to personal preferences ͑Zakis et Chalupper et al, 2009͒. Settings that are beneficial in one environment ͑e.g., noise reduction in a noisy airplane͒ may be detrimental in other environments ͑e.g., listening to music in a quiet surrounding͒.…”

Section: Introductionmentioning

confidence: 99%

Adaptive environment classification system for hearing aids

Lamarche

Giguère

Gueaieb

et al. 2010

The Journal of the Acoustical Society of America

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An adaptive sound classification framework is proposed for hearing aid applications. The long-term goal is to develop fully trainable instruments in which both the acoustical environments encountered in daily life and the hearing aid settings preferred by the user in each environmental class could be learned. Two adaptive classifiers are described, one based on minimum distance clustering and one on Bayesian classification. Through unsupervised learning, the adaptive systems allow classes to split or merge based on changes in the ongoing acoustical environments. Performance was evaluated using real-world sounds from a wide range of acoustical environments. The systems were first initialized using two classes, speech and noise, followed by a testing period when a third class, music, was introduced. Both systems were successful in detecting the presence of an additional class and estimating its underlying parameters, reaching a testing accuracy close to the target rates obtained from best-case scenarios derived from non-adaptive supervised versions of the classifiers (about 3% lower performance). The adaptive Bayesian classifier resulted in a 4% higher overall accuracy upon splitting adaptation than the minimum distance classifier. Merging accuracy was found to be the same in the two systems and within 1%-2% of the best-case supervised versions.

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“…Various algorithms for categorizing acoustic environments or statistical models for deriving the preferred responses have been described for use in hearing aids (e.g., Alexandre, Cauadra, Alvarez, Rosa-Zurera, & Lopez-Ferreras, 2006;Chalupper, 2006;Chalupper, Junius, & Powers, 2009;Dijkstra, Ypma, de Vries, & Leenen, 2007;Lamarche, Giguere, Gueaieb, Aboulnasr, & Othman, 2010;Rahal, 2010;Zakis et al, 2007;Zakis, McDermott, & Fisher, 2001). As mentioned earlier, Zakis et al (2007) was a Level 4 study.…”

Section: How Well Does a Hearing Aid Algorithm Converge To The Users'mentioning

confidence: 99%

“…As mentioned earlier, Zakis et al (2007) was a Level 4 study. Level of study was not assigned to other studies reviewed in this section because some studies did not provide sufficient information on the methodologies that were used to evaluate the outcomes with these algorithms (e.g., Chalupper 2006;Chalupper et al, 2009) andRahl (2010) trialed the algorithm on simulated users only. Other studies have only described the algorithms without any outcome data (e.g., Alexandre et al, 2006;Büchler et al, 2005;Dijkstra et al, 2007;Lamarche et al, 2010;Zakis et al, 2001).…”

Section: How Well Does a Hearing Aid Algorithm Converge To The Users'mentioning

confidence: 99%

“…Other studies have only described the algorithms without any outcome data (e.g., Alexandre et al, 2006;Büchler et al, 2005;Dijkstra et al, 2007;Lamarche et al, 2010;Zakis et al, 2001). Trainable parameters could include compression threshold, compression ratio, gain below compression threshold, frequency shaping, noise suppression, microphone mode, and spectral enhancement in addition to gain adjustments (Chalupper et al, 2009;Dreschler et al, 2008;Taylor, 2011;Zakis et al, 2007).…”

Section: How Well Does a Hearing Aid Algorithm Converge To The Users'mentioning

confidence: 99%

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Evidence on Self-Fitting Hearing Aids

Wong

2011

Trends in Amplification

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The research on self-fitting hearing aids is reviewed using evidence-based principles. The evaluation begins with a definition of the research questions followed by a detailed search of the literature and then a review of the relevant studies. Four features of self-fitting hearing aids are reviewed: in-situ threshold measurement, whether an initial fitting prescribed using standard prescription formulae will approximate user preferences, outcomes with training of hearing aids for preferred responses, and assembly and use of the aids. There is at least good quality evidence suggesting that in-situ thresholds can be reliably obtained, that prescribed initial fittings approximate preferred responses, and that users are able to train the hearing aids and would prefer the trained responses. However, evidence on other outcomes and the ability of users to assemble and use such instruments is limited. Gaps in research with self-fitting hearing aids are identified.

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Algorithm lets users train aid to optimize compression, frequency shape, and gain

Cited by 13 publications

References 9 publications

Smartphone-Based System for Learning and Inferring Hearing Aid Settings

Smartphone-Based System for Learning and Inferring Hearing Aid Settings

Adaptive environment classification system for hearing aids

Evidence on Self-Fitting Hearing Aids

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