Using genetic algorithms with subjective input from human subjects: implications for fitting hearing aids and cochlear implants.
OBJECTIVE:: To present a comprehensive analysis of the feasibility of genetic algorithms (GA) for finding the best fit of hearing aids or cochlear implants for individual users in clinical or research settings, where the algorithm is solely driven by subjective human input. DESIGN:: Due to varying p...
| Published in: | Ear & Hearing (01960202) Vol. 28; no. 3; pp. 370 - 381 |
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| Main Authors: | , , |
| Format: | research tables/charts Journal Article |
| Published: |
Lippincott Williams & Wilkins
2007 Jun
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105969717&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105969717 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01960202 IM7 jtl: Ear & Hearing (01960202) issn: 01960202 maglogo: N pubinfo: dt: 2007 Jun vid: 28 iid: 3 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 105969717 105969717 2009586024 10.1097/aud.0b013e318047935e NLM17485986 105969717 ppf: 370 ppct: 11 formats: tig: atl: Using genetic algorithms with subjective input from human subjects: implications for fitting hearing aids and cochlear implants. aug: au: Baskent D Eiler CL Edwards B affil: Starkey Hearing Research Center, Berkeley, California. sug: subj: Algorithms Cochlea Physiopathology Cochlear Implant Programming Genetics Speech Perception Adult Auditory Threshold Confidence Intervals Descriptive Statistics Genes Hearing Loss, Sensorineural Physiopathology Paired T-Tests Prosthesis Design Reflex, Acoustic Speech Discrimination Tests Two-Tailed Test Human Adult: 19-44 years ab: OBJECTIVE:: To present a comprehensive analysis of the feasibility of genetic algorithms (GA) for finding the best fit of hearing aids or cochlear implants for individual users in clinical or research settings, where the algorithm is solely driven by subjective human input. DESIGN:: Due to varying pathology, the best settings of an auditory device differ for each user. It is also likely that listening preferences vary at the same time. The settings of a device customized for a particular user can only be evaluated by the user. When optimization algorithms are used for fitting purposes, this situation poses a difficulty for a systematic and quantitative evaluation of the suitability of the fitting parameters produced by the algorithm. In the present study, an artificial listening environment was generated by distorting speech using a noiseband vocoder. The settings produced by the GA for this listening problem could objectively be evaluated by measuring speech recognition and comparing the performance to the best vocoder condition where speech was least distorted. Nine normal-hearing subjects participated in the study. The parameters to be optimized were the number of vocoder channels, the shift between the input frequency range and the synthesis frequency range, and the compression-expansion of the input frequency range over the synthesis frequency range. The subjects listened to pairs of sentences processed with the vocoder, and entered a preference for the sentence with better intelligibility. The GA modified the solutions iteratively according to the subject preferences. The program converged when the user ranked the same set of parameters as the best in three consecutive steps. The results produced by the GA were analyzed for quality by measuring speech intelligibility, for test-retest reliability by running the GA three times with each subject, and for convergence properties. RESULTS:: Speech recognition scores averaged across subjects were similar for the best vocoder solution and for the solutions produced by the GA. The average number of iterations was 8 and the average convergence time was 25.5 minutes. The settings produced by different GA runs for the same subject were slightly different; however, speech recognition scores measured with these settings were similar. Individual data from subjects showed that in each run, a small number of GA solutions produced poorer speech intelligibility than for the best setting. This was probably a result of the combination of the inherent randomness of the GA, the convergence criterion used in the present study, and possible errors that the users might have made during the paired comparisons. On the other hand, the effect of these errors was probably small compared to the other two factors, as a comparison between subjective preferences and objective measures showed that for many subjects the two were in good agreement. CONCLUSIONS:: The results showed that the GA was able to produce good solutions by using listener preferences in a relatively short time. For practical applications, the program can be made more robust by running the GA twice or by not using an automatic stopping criterion, and it can be made faster by optimizing the number of the paired comparisons completed in each iteration. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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