Reasons for ceiling ratings in real-life evaluations of hearing aids: the relationship between SNR and hearing aid ratings

IntroductionIn past Ecological Momentary Assessment (EMA) studies, hearing aid outcome ratings have often been close to ceiling.MethodsTo analyze the underlying reasons for the very positive ratings, we conducted a study with 17 experienced hearing aid wearers who were fitted with study hearing aids. The acceptable noise level and the noise level where participants were unable to follow speech were measured. The participants then rated hearing aid satisfaction, speech understanding and listening effort for pre-defined SNRs between −10 and +20 dB SPL in the laboratory. These ratings were compared to ratings of a two-week EMA trial. Additionally, estimates of SNRs were collected from hearing aids during the EMA trial and we assessed whether the participants experienced those SNRs rated poorly in the laboratory in real life.ResultsThe results showed that for hearing aid satisfaction and speech understanding, the full rating scale was used in the laboratory, while the ratings in real life were strongly skewed towards the positive end of the scale. In the laboratory, SNRs where participants indicated they could not follow the narrator (“unable to follow” noise level) were rated clearly better than the lowest possible ratings. This indicates that very negative ratings may not be applicable in real-life testing. The lower part of the distribution of real-life SNR estimates was related to participants’ individual “unable to follow” noise levels and the SNRs which were rated poorly in the laboratory made up less than 10% of the speech situations experienced in real life.DiscussionThis indicates that people do not seem to frequently experience listening situations at SNRs where they are dissatisfied with their hearing aids and this could be the reason for the overly positive hearing aid outcome ratings in EMA studies. It remains unclear to what extent the scarcity of such situations is due lack of encounters or intentional avoidance

An intuitive model of perceptual grouping for HCI design

ABSTRACT Understanding and exploiting the abilities of the human visual system is an important part of the design of usable user interfaces and information visualizations. Good design enables quick, easy and veridical perception of key components of that design. An important facet of human vision is its ability to seemingly effortlessly perform "perceptual organization"; it transforms individual feature estimates into perception of coherent regions, structures, and objects. We perceive regions grouped by proximity and feature similarity, grouping of curves by good continuation, and grouping of regions of coherent texture. In this paper, we discuss a simple model for a broad range of perceptual grouping phenomena. It takes as input an arbitrary image, and returns a structure describing the predicted visual organization of the image. We demonstrate that this model can capture aspects of traditional design rules, and predicts visual percepts in classic perceptual grouping displays

Optimality of Human Contour Integration

For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy

Contour Integration Models Predicting Human Behavior

Contour integration is believed to be a fundamental process inobject recognition and image segmentation. However, its neuronalmechanisms are still not well understood. Psychophysical experimentsshowed that humans are remarkably efficient in integrating contourseven if these are jittered or partially occluded. Therefore thebrain requires a reliable algorithm for extracting contours fromstimuli. Several recent publications demonstrated that the brainoften uses optimal strategies to integrate sensory information.Hence in this thesis I want to tackle the question which contourintegration model describes human contour integration best.Mathematically, contour ensembles can be characterized by aconditional link probability density between oriented edge elements,termed an association field. This association field can be used togenerate contours or vice versa to extract a contour from astimulus. While in most neuronal network models all inputs to aneuron are summed up, in such a probabilistically motivated neuralnetwork for contour integration the afferent input due to the visualstimuli and the lateral input from horizontal network interactionsare multiplied.Long-range horizontal interactions in primary visual cortex linkorientation columns with similar preferred orientations and areoftenassumed to be the neuronal substrate for the association field. Experimental findings in monkeys suggest isotropic long-rangehorizontal connections, spreading symmetrically into all directionsfrom an orientation column. In contrast, probabilistic modelsrequire unidirectional lateral interactions, linking orientationcolumns in only one direction, in order to get optimal contourdetection performance.Using stimuli generated from given association fields, our numericalsimulations show that contour detection performance for both,probabilistic-multiplicative as well as additive models reacheshuman performance. Hence detection performance alone is insufficientto rule out either model class. However, psychophysical experimentswith humans reveal that contour detection errors are not maderandomly, but are highly correlated among different subjects. Thus amodel describing contour integration in the brain should not onlyexplain human contour detection performance, but should alsoreproduce these systematic errors made by humans. Comparison betweenmisdetections of humans and mispredictions of the models on atrial-by-trial basis was used to evaluate different model dynamicsand association fields. This suggests that unidirectionalmultiplicatively coupled horizontal interactions are required inorder to explain human behavior. Furthermore, cortical magnificationfactors have to be taken into account and a fixed association fieldgeometry for all stimuli is preferable instead of using for eachcontour the association field employed for the generation of thiscontour

Training listeners for multi-channel audio quality evaluation in MUSHRA with a special focus on loop setting

Audio quality evaluation for audio material of intermediate and high quality requires expert listeners. In comparison to non-experts, these are not only more critical in their ratings, but also employ different strategies in their evaluation. In particular they concentrate on shorter sections of the audio signal and compare more to the reference than inexperienced listeners. We created a listener training for detecting coding artifacts in multi-channel audio quality evaluation. Our training is targeted at listeners without technical background. For this training, expert listeners commented on smaller sections of an audio signal they focused on in the listening test and provided a description of the artifacts they perceived. The non-expert listeners participating in the training were provided with general advice for helpful strategies in MUSHRA tests (Multi Stimulus Tests with Hidden Reference and Anchor), with the comments on specific sections of the stimulus by the experts, and with feedback after rating. Listener's performance improved in the course of the training session. Afterwards they performed the same test without the training material and a further test with different items. Performance did not decrease in these tests, showing that they could transfer what they had learned to other stimuli. After the training they also set more loops and compared more to the reference

Konturintegrationsmodelle zur Vorhersage menschlichen Verhaltens

Contour integration is believed to be a fundamental process inobject recognition and image segmentation. However, its neuronalmechanisms are still not well understood. Psychophysical experimentsshowed that humans are remarkably efficient in integrating contourseven if these are jittered or partially occluded. Therefore thebrain requires a reliable algorithm for extracting contours fromstimuli. Several recent publications demonstrated that the brainoften uses optimal strategies to integrate sensory information.Hence in this thesis I want to tackle the question which contourintegration model describes human contour integration best.Mathematically, contour ensembles can be characterized by aconditional link probability density between oriented edge elements,termed an association field. This association field can be used togenerate contours or vice versa to extract a contour from astimulus. While in most neuronal network models all inputs to aneuron are summed up, in such a probabilistically motivated neuralnetwork for contour integration the afferent input due to the visualstimuli and the lateral input from horizontal network interactionsare multiplied.Long-range horizontal interactions in primary visual cortex linkorientation columns with similar preferred orientations and areoftenassumed to be the neuronal substrate for the association field. Experimental findings in monkeys suggest isotropic long-rangehorizontal connections, spreading symmetrically into all directionsfrom an orientation column. In contrast, probabilistic modelsrequire unidirectional lateral interactions, linking orientationcolumns in only one direction, in order to get optimal contourdetection performance.Using stimuli generated from given association fields, our numericalsimulations show that contour detection performance for both,probabilistic-multiplicative as well as additive models reacheshuman performance. Hence detection performance alone is insufficientto rule out either model class. However, psychophysical experimentswith humans reveal that contour detection errors are not maderandomly, but are highly correlated among different subjects. Thus amodel describing contour integration in the brain should not onlyexplain human contour detection performance, but should alsoreproduce these systematic errors made by humans. Comparison betweenmisdetections of humans and mispredictions of the models on atrial-by-trial basis was used to evaluate different model dynamicsand association fields. This suggests that unidirectionalmultiplicatively coupled horizontal interactions are required inorder to explain human behavior. Furthermore, cortical magnificationfactors have to be taken into account and a fixed association fieldgeometry for all stimuli is preferable instead of using for eachcontour the association field employed for the generation of thiscontour

Comparing hearing aid programs using Ecological Momentary Assessment: direct versus indirect comparison

Here you can find the questionnaire data for the conference paper "Comparing hearing aid programs using Ecological Momentary Assessment: direct versus indirect comparison" to be presented at the International Symposium for Hearing 202

Remote EMA study about modification and avoidance of difficult listening situations by hearing aid users

https://www.internetaudiology.com/2021/?p=presentation

Intelligibility evaluation of speech coding standards in severe background noise and packet loss conditions

Speech intelligibility is an important aspect of speech transmission but often only the quality is evaluated using perceptual tests when speech coding standards are compared. In this study, the performance of three wideband speech coding standards, adaptive multi-rate wideband (AMR-WB), G.718, and enhanced voice services (EVS), is evaluated in a subjective intelligibility test. The test covers different packet loss conditions as well as a near-end background noise condition. Additionally, an objective quality evaluation in different packet loss conditions is conducted. All of the test conditions extend beyond the specification range to evaluate the attainable performance of the codecs in extreme conditions. The results of the subjective tests show that both EVS and G.718 are better in terms of intelligibility than AMR-WB. EVS attains the same performance as G.718 with lower algorithmic delay