Individual preference for the amount of noise reduction

Houben, Rolph; Dijkstra, Tjeerd M. H.; Dreschler, Wouter A.

doi:10.21437/pqs.2010-17

Cited by 5 publications

(25 citation statements)

References 13 publications

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“…For the participant in (A) the overall preference rating is stronger influenced by noise annoyance, while the overall preference rating of the participant in (B) is stronger influenced by speech naturalness. These different preference profiles have been reported previously in literature and can as yet not be related to any objective or subjective measure (Neher and Wagener 2016;Houben, Dijkstra, and Dreschler 2013;Brons, Houben, and Dreschler 2013). For participant (C) however, the ratings of all judgement criteria are rather alike.…”

Section: Q2: Do Individual Preferences Differ Between Listeners?supporting

confidence: 68%

“…Several studies have shown large inter-individual differences in subjective listening experiments which implies that group averages may not sufficiently represent the individual preferences (Houben, Dijkstra, and Dreschler 2013;Neuman et al 1995). The repeated measures ANOVA of the estimated worth parameters of all listeners showed a significant interaction of processing condition with subject (Table 2) for all judgement criteria.…”

Section: Q2: Do Individual Preferences Differ Between Listeners?mentioning

confidence: 97%

“…Examples of such NR parameters are the type of noise estimator (Brons, Houben, and Dreschler 2012), the interaction with other HA signal processing such as compression (Kortlang et al 2018), or gain reduction strength. Regarding the latter, previous research on perceptual effects has shown a large variability of preferences for the maximum strength of gain reduction between listeners (Houben, Dijkstra, and Dreschler 2013;Neher and Wagener 2016). Due to noise estimation errors and processing artefacts, stronger noise reduction is accompanied with distortions that reduce speech quality (Bentler et al 2008).…”

Section: Introductionmentioning

confidence: 98%

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Subjective evaluation of single microphone noise reduction with different time constants

Reinten

Ronde-Brons

Houben³

et al. 2019

International Journal of Audiology

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Objective: Previous studies on single microphone noise reduction (NR) in hearing aids (HAs) have shown that some NR algorithms provide beneficial effects in terms of listener preference. To improve HA user satisfaction, we are interested in characteristics that determine preferences for NR, and in the interindividual variability. The aim of this study was to test if dynamic properties of NR influence listener preference. Design: The gain reduction at speech offsets of a NR algorithm was slowed down by applying temporal exponential smoothing. At speech onsets the gain recovery was left unchanged. Test signals consisted of speech in continuous and modulated speech-shaped background noise, processed with three time constants: 0, 100, and 200 ms. Study sample: 16 Normal hearing (NH) and 16 hearing impaired (HI) subjects participated in a pairedcomparison listening test. Results: NH subjects as a group had a significant preference for NR with time constants of 100, or 200 ms (slower acting NR). HI listeners as a group preferred NR over no NR, but had no clear preference for fast or slow NR. Patterns of preference differed between individual listeners. Conclusions: NR dynamics had an impact on individual listener preference and should be considered when optimising HAs.

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Section: Q2: Do Individual Preferences Differ Between Listeners?supporting

confidence: 68%

Section: Q2: Do Individual Preferences Differ Between Listeners?mentioning

confidence: 97%

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Subjective evaluation of single microphone noise reduction with different time constants

Reinten

Ronde-Brons

Houben³

et al. 2019

International Journal of Audiology

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“…We recorded hearing-aid output of three linearly fitted hearing aids from different manufacturers (Phonak Exélia M, ReSound Azure AZ80-DVI, and Widex Mind 440) using the method described by Houben, Brons, and Dreschler (2011) . Analyses of recordings from these hearing aids fitted for different hearing losses revealed that noise-reduction processing in this selection of hearing aids was independent of hearing loss (i.e., the patterns of gain reduction remained the same for the same input signals when the hearing aids were fitted for another hearing loss), so that it was not necessary for the current purpose to fit the hearing aids to other targets than in Houben et al.…”

Section: Methodsmentioning

confidence: 99%

Effects of Noise Reduction on Speech Intelligibility, Perceived Listening Effort, and Personal Preference in Hearing-Impaired Listeners

2014

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This study evaluates the perceptual effects of single-microphone noise reduction in hearing aids. Twenty subjects with moderate sensorineural hearing loss listened to speech in babble noise processed via noise reduction from three different linearly fitted hearing aids. Subjects performed (a) speech-intelligibility tests, (b) listening-effort ratings, and (c) paired-comparison ratings on noise annoyance, speech naturalness, and overall preference. The perceptual effects of noise reduction differ between hearing aids. The results agree well with those of normal-hearing listeners in a previous study. None of the noise-reduction algorithms improved speech intelligibility, but all reduced the annoyance of noise. The noise reduction that scored best with respect to noise annoyance and preference had the worst intelligibility scores. The trade-off between intelligibility and listening comfort shows that preference measurements might be useful in addition to intelligibility measurements in the selection of noise reduction. Additionally, this trade-off should be taken into consideration to create realistic expectations in hearing-aid users.

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“…Investigation of signal enhancement was conducted in [19], towards exploring the individual preferences of humans for noise reduction algorithms and a new metric was proposed as well. In fact, this approach was considered quite useful for experimental assessment and as a result was adopted in the current work.…”

Section: Best Regards the Authors 0 Introductionmentioning

confidence: 99%

Improving Multilingual Interaction for Consumer Robots through Signal Enhancement in Multichannel Speech

Tsardoulias¹,

Thallas²,

Symeonidis³

et al. 2016

J. Audio Eng. Soc.

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Social robotics have become a trend in contemporary robotics research, since they can be successfully used in a wide range of applications. One of the most fundamental communication skills a consumer robot must have is the oral interaction with a human, in order to provide feedback or accept commands. There are quite a few well established Automatic Speech Recognition (ASR) tools, however without providing efficient results, especially in less popular languages, and more importantly under noisy conditions. The current paper investigates different voice activity detection and noise elimination methodologies to be used with ASRbased oral interaction with an affordable budget robot, NAO v4. Acoustically semi-stationary environments are assumed, which in conjunction to the high background noise of the NAO's microphones make the ASR quite difficult to succeed. Full Title:Improving multilingual interaction for consumer robots through signal enhancement in multichannel speechAdditional Information: Question ResponseHas this article previously been published at a conference or convention?No Please enter the text of an appropriate cover letter to accompany your submission, briefly describing the article you want to submit and saying why you believe it is suitable for publication in the AES Journal.Within the context of this paper several single and multi-channel VAD/noise estimation and noise elimination strategies were implemented and evaluated towards ASR in acoustically semi-stationary environments. This work aspires to be assistive in the consumer robotics domain, since the utilization of expensive hardware (microphones or robots in general) is usually out of the question, thus a robust VAD and noise reduction approach should exist. The main novelty of this paper does not lie in the implementations per se but in the adaptation approach followed, aiming to optimize the parameters of each method in order to improve ASR results. Concluding, we believe that the current work is eligible for submission in the AES Journal, since it researches a realistic problem current consumer robots have, from a signal processing point of view.Social robotics have become a trend in contemporary robotics research, since they can be successfully used in a wide range of applications. One of the most fundamental communication skills a consumer robot must have is the oral interaction with a human, in order to provide feedback or accept commands. There are quite a few well established Automatic Speech Recognition (ASR) tools, without however providing efficient results, especially in less popular languages, and more importantly under noisy conditions. The current paper investigates different voice activity detection and noise elimination methodologies to be used with ASRbased oral interaction with an affordable budget robot, NAO v4. Acoustically quasi-stationary environments are assumed, which in conjunction with the high background noise of the NAO's microphones make the ASR quite difficult to succeed. 8

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Individual preference for the amount of noise reduction

Cited by 5 publications

References 13 publications

Subjective evaluation of single microphone noise reduction with different time constants

Subjective evaluation of single microphone noise reduction with different time constants

Effects of Noise Reduction on Speech Intelligibility, Perceived Listening Effort, and Personal Preference in Hearing-Impaired Listeners

Improving Multilingual Interaction for Consumer Robots through Signal Enhancement in Multichannel Speech

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