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Record W4391143920 · doi:10.1097/aud.0000000000001469

The Effects of Signal to Noise Ratio, T 60, Wide-Dynamic Range Compression Speed, and Digital Noise Reduction in a Virtual Restaurant Setting

2024· article· en· W4391143920 on OpenAlexaff
Gregory M. Ellis, Jeff Crukley, Pamela E. Souza

Bibliographic record

VenueEar and Hearing · 2024
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsMcMaster UniversityUniversity of Toronto
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsReverberationActive listeningHearing aidComputer scienceIntelligibility (philosophy)Noise reductionDynamic rangeHeadphonesPsychoacousticsAmbient noise levelHearing lossBackground noiseSpeech recognitionAcousticsAudiologyPsychologyPerceptionTelecommunicationsArtificial intelligenceCommunicationComputer vision

Abstract

fetched live from OpenAlex

OBJECTIVES: Hearing aid processing in realistic listening environments is difficult to study effectively. Often the environment is unpredictable or unknown, such as in wearable aid trials with subjective report by the wearer. Some laboratory experiments create listening environments to exert tight experimental control, but those environments are often limited by physical space, a small number of sound sources, or room absorptive properties. Simulation techniques bridge this gap by providing greater experimental control over listening environments, effectively bringing aspects of the real-world into the laboratory. This project used simulation to study the effects of wide-dynamic range compression (WDRC) and digital noise reduction (DNR) on speech intelligibility in a reverberant environment with six spatialized competing talkers. The primary objective of this study was to determine the efficacy of WDRC and DNR in a complex listening environment using virtual auditory space techniques. DESIGN: Participants of greatest interest were listeners with hearing impairment. A group of listeners with clinically normal hearing was included to assess the effects of the simulation absent the complex effects of hearing loss. Virtual auditory space techniques were used to simulate a small restaurant listening environment with two different reverberation times (0.8 and 1.8 sec) in a range of signal to noise ratios (SNRs) (-8.5 to 11.5 dB SNR). Six spatialized competing talkers were included to further enhance realism. A hearing aid simulation was used to examine the degree to which speech intelligibility was affected by slow and fast WDRC in conjunction with the presence or absence of DNR. The WDRC and DNR settings were chosen to be reasonable estimates of hearing aids currently available to consumers. RESULTS: A WDRC × DNR × Hearing Status interaction was observed, such that DNR was beneficial for speech intelligibility when combined with fast WDRC speeds, but DNR was detrimental to speech intelligibility when WDRC speeds were slow. The pattern of the WDRC × DNR interaction was observed for both listener groups. Significant main effects of reverberation time and SNR were observed, indicating better performance with lower reverberation times and more positive SNR. CONCLUSIONS: DNR reduced low-amplitude noise before WDRC-amplified the low-intensity portions of the signal, negating one potential downside of fast WDRC and leading to an improvement in speech intelligibility in this simulation. These data suggest that, in some real-world environments that include both reverberation and noise, older listeners with hearing impairment may find speech to be more intelligible if DNR is activated when the hearing aid has fast compression time constants. Additional research is needed to determine the appropriate DNR strength and to confirm results in wearable hearing aids and a wider range of listening environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.266
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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