Instrumental and Perceptual Evaluation of Hearing Devices Methods and Applications
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1 Instrumental and Perceptual Evaluation of Hearing Devices Methods and Applications Christoph Völker Disputation Oldenburg,
2 Evaluation of Hearing Devices Table of Contents 1) Introduction 2) Comparing Binaural Pre-processing Strategies 3) Modifications of the MUlti Stimulus test with Hidden Reference and Anchor (MUSHRA) 4) Unsupervised parameter optimization for hearing aid algorithms 5) Trait-based Hearing aid fitting and fine-tuning C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 2
![) Hougaard & Ruf 2011: 13,1% of German population are hearing impaired (self stated) [highest relative proportion of elderly people in Europe] Mick et al.](/docs-images/75/72128682/images/3-2.jpg ", 2014: Hearing impairment serious impact on the life of the affected persons (social isolation) Solution approach: Digital Hearing Aids Aim: Restauration of")
3 Introduction Facts about hearing impairments Fact sheet of WHO from 2015: 360 million people worldwide have a disabling hearing loss (328 million adults & 32 million children) Over 5% of the world s population (~ 7200 mio.) Hougaard & Ruf 2011: 13,1% of German population are hearing impaired (self stated) [highest relative proportion of elderly people in Europe] Mick et al., 2014: Hearing impairment serious impact on the life of the affected persons (social isolation) Solution approach: Digital Hearing Aids Aim: Restauration of ability to communicate C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 3
4 Introduction Digital Hearing Aids microphones signal processing output signal Digital hearing aids often have multidimensional algorithms settings More than one parameter can be adjusted Example: Two parameters ( screws ) with 10 possible settings each 100 possible configurations in total C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 4
5 Introduction Matching of Device and User complex system Individual users/patients with individual hearing loss and preferences Boon & Bane of Multidimensionality: Device can be fitted to each individual hearing loss and preference Device has too many setting possibilities to test with real persons C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 5
6 Evaluation of Hearing Devices Two Information Sources Evaluation of Hearing Devices instrumental perceptual Machine (Models) Humans C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 6
7 Evaluation of Hearing Devices Table of Contents 1) Introduction 2) Comparing Binaural Pre-processing Strategies 3) Modifications of the MUlti Stimulus test with Hidden Reference and Anchor (MUSHRA) 4) Unsupervised parameter optimization for hearing aid algorithms 5) Trait-based Hearing aid fitting and fine-tuning C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 7
8 Comparing Binaural Pre-processing Strategies In short Comparison of eight signal pre-processing strategies Perceptual assessment: SRTs for NH and HI Instrumental assessment: isnr, STOI, PESQ, BSIM Analysis of correlation shows that single auditory model-based measures are not able to predict the algorithm benefit for the individual aided users/patients satisfactorily C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 8
9 Evaluation of Hearing Devices Chapter I - Outcome Evaluation of Hearing Devices discrepancies Machine (Models) Humans C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 9
10 Evaluation of Hearing Devices Aim of Thesis Evaluation of Hearing Devices discrepancies Machine (Models) Humans C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 10
11 Evaluation of Hearing Devices Chapter I - Outcome Published in Trends in Hearing, Vol. 19, C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 11
12 Evaluation of Hearing Devices Table of Contents 1) Introduction 2) Comparing Binaural Pre-processing Strategies 3) Modifications of the MUlti Stimulus test with Hidden Reference and Anchor (MUSHRA) 4) Unsupervised parameter optimization for hearing aid algorithms 5) Trait-based Hearing aid fitting and fine-tuning C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 12
13 Evaluation of Hearing Devices Chapter II Evaluation of Hearing Devices discrepancies Machine (Models) Humans C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 13
14 Modifications of the MUSHRA listening test MUSHRA C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 14
15 Modifications of the MUSHRA listening test Objectives Measurement practice at Hörzentrum Oldenburg shows that elder and technically unexperienced participants have problems with this test method concerning usability. Objectives of this study: Introduction and evaluation of two new GUIs for the MUSHRA test to increase accessibility and reliability ideally not influencing the results of ratings C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 15
16 Modifications of the MUSHRA listening test MUSHRA (simple) C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 16
17 Modifications of the MUSHRA listening test MUSHRA (simple) C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 17
18 Modifications of the MUSHRA listening test MUSHRA (drag & drop) C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 18
19 Modifications of the MUSHRA listening test MUSHRA (drag & drop) C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 19
20 Modifications of the MUSHRA listening test Subject factors Factors: age (young, old) hearing (normal, impaired) technical experience (high, low) Groups: G1 G2 G3 G4 G5 young old old old old normal hearing normal hearing normal hearing impaired hearing impaired hearing tech. experienced tech. experienced tech. unexperienced tech. experienced tech. unexperienced C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 20
21 Modifications of the MUSHRA listening test Comparison of Modifications Comparison of MUSHRA Modifications by Final Algorithm Ratings/Rankings System Usability Scale (Questionaire) Preference Ranking Duration/Trial egauge C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 21
22 Modifications of the MUSHRA listening test Comparison with egauge Standardized measure ( gauge ) for the expertise ( e- ) of a listener Values for reliability and discrimination ability for each participant Reliability: closeness of repeated ratings Discrimination: ability to perceive differences between stimuli Calculation done by using analysis of variance (ANOVA) By using permutation tests, we get a significance level for each measure C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 22
23 Results Reliability & Discrimination: classic II I IV III C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 23
24 Results Reliability & Discrimination: classic 12/50 doubtable ~24% doubtable data (surprisingly only) 3 technically unexperienced subjects (groups 3 and 5) 8(!) technically experienced subjects (groups 2 and 4) (even) 1 from control group C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 24
25 Results Reliability & Discrimination: simple 19/50 doubtable ~39% doubtable data 9 technically unexperienced subjects (groups 3 and 5) 8 technically experienced subjects (groups 2 and 4) 2 from control group C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 25
26 Results Reliability & Discrimination: drag & drop (only) 8/50 doubtable ~16% doubtable data 6 technically unexperienced subjects (groups 3 and 5) 2 technically experienced subjects (groups 2 and 4) C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 26
27 Modifications of the MUSHRA listening test Differentiated choice of test method Algorithm Ratings & Rankings Duration / Trial doubtable data (including G1) doubtable data of technically unexperienced (G3/G5) doubtable data of technically experienced (G2/G4) CLASSIC SIMPLE DRAG & DROP only minor differences fastest 24% 39% 16% 16% 47% 32% 40% 40% 10% C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 27
28 Modifications of the MUSHRA listening test Conclusions I stay with old school MUSHRA C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 28
29 Modifications of the MUSHRA listening test Conclusions I d rather use drag & drop C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 29
30 Evaluation of Hearing Devices Chapter II - Outcome Evaluation of Hearing Devices discrepancies Machine (Models) Humans Method for subjective testing C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 30
31 Evaluation of Hearing Devices Chapter II - Outcome Accepted by International Journal of Audiology (in press) C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 31
32 Evaluation of Hearing Devices Chapter III Evaluation of Hearing Devices discrepancies Machine (Models) Humans Method for subjective testing C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 32
33 Evaluation of Hearing Devices Table of Contents 1) Introduction 2) Comparing Binaural Pre-processing Strategies 3) Modifications of the MUlti Stimulus test with Hidden Reference and Anchor (MUSHRA) 4) Unsupervised parameter optimization for hearing aid algorithms 5) Trait-based Hearing aid fitting and fine-tuning C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 33
34 Unsupervised parameter optimization In short Bane of multidimensional hearing aid algorithms Automated/Unsupervised model-based approach calculates percentile contours of instrumental measures within algorithm parameter space finds optimal parameter combinations in intersections of contours Here: coherence-based noise reduction scheme (two parameters: alpha, tau) equally weighting of four instrumental measures C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 34
35 Unsupervised parameter optimization Percentile Contours C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 35
36 Unsupervised parameter optimization Percentile Contours C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 36
37 Unsupervised parameter optimization Percentile Contours C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 37
38 Unsupervised parameter optimization Intersection C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 38
39 Unsupervised parameter optimization Minimal Intersection C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 39
40 Unsupervised parameter optimization Minimal Intersection C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 40
41 Unsupervised parameter optimization Results: Instrumental evaluation Comparison with two manual parameter sets Instr. enhancements over Input SNRs Automated Set performs best (or equally) for SNR < 5 db C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 41
42 Unsupervised parameter optimization Results: SRT measurements NoPre: No Pre-Processing All settings: No SRT-benefit regarding NoPre SRT-Ranking: 1) NoPre / Manual Set II 2) Manual Set I 3) Automated Set C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 42
43 Unsupervised parameter optimization Conclusion Optimization method needs some more development a) choice of models is crucial (SII = BINSII, global SNR, PESQ, HASQI ) b) coherence scheme might not be optimal for demonstration purposes c) not use compromise setting (equal weighting), but weight the models differently a) b) c) C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 43
44 Evaluation of Hearing Devices Chapter III - Outcome Evaluation of Hearing Devices discrepancies Machine (Models) Humans Method for optimized settings Method for subjective testing C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 44
45 Evaluation of Hearing Devices Chapter III - Outcome C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 45
46 Evaluation of Hearing Devices Table of Contents 1) Introduction 2) Comparing Binaural Pre-processing Strategies 3) Modifications of the MUlti Stimulus test with Hidden Reference and Anchor (MUSHRA) 4) Unsupervised parameter optimization for hearing aid algorithms 5) Trait-based Hearing aid fitting and fine-tuning C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 46
47 Trait-based Hearing aid fitting and fine-tuning Introduction Traits? Noise haters or distortion haters (Houben, 2013): different attitudes against noise or introduced distortions/artifacts Power Junkies Hearing aid fitting compromise between aspects as speech quality and intelligibility users have different preferences for those trade-offs (Abrams et al., 2011) Assumption: Judgments regarding optimal algorithm settings (or different algorithms) by linear combination of individually weighted subjective traits C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 47
48 Trait-based Hearing aid fitting and fine-tuning Concept: Learning phase e.g. a prototypical distortion hater trait-based: e.g. punish distortions combined discrimination and classification task (CoDiCl) generate stimuli with different distortion levels: same ratings? C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 48
49 Trait-based Hearing aid fitting and fine-tuning Concept: Application phase When a sufficient agreement between objective and subjective assessment is found, i.e., models correlate with traits Hearing Aid Fitting: Usage of trait-based models with different weightings to differentiate between individual user preferences Prescription of not one single setting/preset, but a managable amount of settings from a reasonable path of probation User then finds preferred setting with an interactive intuitive procedure psychological ownership (Dillon et al., 2006) IKEA effect (Norton et al., 2012) C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 49
50 Trait-based Hearing aid fitting and fine-tuning Exemplary application coherence NR scheme contours of two (trait-based) measures noise hating: SII distortion hating: PSM/PEMO-Q 21 different initial weightings 100 / 0 95 / 5 90 / / C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 50
51 Trait-based Hearing aid fitting and fine-tuning Exemplary application C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 51
52 Evaluation of Hearing Devices Chapter IV - Outcome Evaluation of Hearing Devices discrepancies Machine (Models) Humans Method for optimized settings Method for subjective testing C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 52
53 Evaluation of Hearing Devices Chapter IV - Outcome C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 53
54 Acknowledgment Co-Authors C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 54
55 Acknowledgment Funding Funded by: - BMBF "Modellbasiertes Hörgerät" (01EZ1127D) - DFG Exzellenzcluster Hearing4All (EXZ1077) - DFG Forschergruppe (FOR1732)
56 Evaluation of Hearing Devices Aim of Thesis & Outcome Evaluation of Hearing Devices discrepancies Machine (Models) Humans Method for optimized settings Method for subjective testing C. Völker: Instrumental and Perceptual Evaluation of Hearing Devices 56
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