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Record W2034757611 · doi:10.1097/aud.0b013e3182a4a6f9

Analysis of Spatiotemporal Pattern Correction Using a Computational Model of the Auditory Periphery

2013· article· en· W2034757611 on OpenAlexaff
Timothy Zeyl, Ian C. Bruce

Bibliographic record

VenueEar and Hearing · 2013
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLoudnessFormantVowelComputer scienceSpeech recognitionMetric (unit)Intelligibility (philosophy)

Abstract

fetched live from OpenAlex

In Brief Objectives: The purpose of this study was to determine the cause of poor experimental performance of a spatiotemporal pattern correction (SPC) scheme that has been proposed as a hearing aid algorithm and to determine contexts in which it may provide benefit. The SPC scheme is intended to compensate for altered phase response and group delay differences in the auditory nerve spiking patterns in impaired ears. Based on theoretical models of loudness and the hypothesized importance of temporal fine structure for intelligibility, the compensations of the SPC scheme are expected to provide benefit; however, preliminary experiments revealed that listeners preferred unprocessed or minimally processed speech as opposed to complete SPC processed speech. Design: An improved version of the SPC scheme was evaluated with a computational auditory model in response to a synthesized vowel at multiple SPLs. The impaired model auditory nerve response to SPC-aided stimuli was compared to the unaided stimuli for spectrotemporal response similarity to the healthy auditory model. This comparison included analysis of synchronized rate across auditory nerve characteristic frequencies and a measure of relative phase response of auditory nerve fibers to complex stimuli derived from cross-correlations. Results: Analysis indicates that SPC can improve a metric of relative phase response at low SPLs, but may do so at the cost of decreased spectrotemporal response similarity to the healthy auditory model and degraded synchrony to vowel formants. In-depth analysis identifies several technical and conceptual problems associated with SPC that need to be addressed. These include the following: (1) a nonflat frequency response through the analysis–synthesis filterbank that results from time-varying changes in the relative temporal alignment of filterbank channels, (2) group delay corrections that are based on incorrect frequencies because of spread of synchrony in auditory nerve responses, and (3) frequency modulations in the processed signal created by the insertion of delays. Conclusions: Despite these issues, SPC provided benefit to an error metric derived from auditory nerve response cross-correlations at low SPLs, which may mean phase adjustment is achieved at the expense of other metrics, but could be beneficial for low-level speech. Spatiotemporal pattern correction is a hearing aid algorithm proposed to account for impaired auditory nerve phase response and shortened group delays; however, listening experiments have yielded mixed results. This study employs a computational model of the auditory periphery and discovers that impaired group delays might be adjusted at the cost of degraded synchrony and decreased similarity in the temporal fine structure of the impaired auditory nerve response compared to the healthy response. However, at low SPLs these degradations are small and relative phase of response is most improved, suggesting that spatiotemporal pattern correction is most beneficial at low levels.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.112

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 head0.053
GPT teacher head0.278
Teacher spread0.225 · 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 designSimulation or modeling
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

Citations0
Published2013
Admission routes1
Has abstractyes

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