MétaCan
Menu
Back to cohort
Record W2313003104 · doi:10.1097/aud.0000000000000112

Development of the Adaptive Music Perception Test

2014· article· en· W2313003104 on OpenAlexaff
Martin Kirchberger, Frank Russo

Bibliographic record

VenueEar and Hearing · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsToronto Metropolitan University
FundersEidgenössische Technische Hochschule Zürich
KeywordsTimbrePerceptionChord (peer-to-peer)PsychologyAudiologyLoudnessTest (biology)Pitch (Music)Context (archaeology)Auditory perceptionSpeech recognitionComputer scienceMusical

Abstract

fetched live from OpenAlex

OBJECTIVES: Despite vast amounts of research examining the influence of hearing loss on speech perception, comparatively little is known about its influence on music perception. No standardized test exists to quantify music perception of hearing-impaired (HI) persons in a clinically practical manner. This study presents the Adaptive Music Perception (AMP) test as a tool to assess important aspects of music perception with hearing loss. DESIGN: A computer-driven test was developed to determine the discrimination thresholds of 10 low-level physical dimensions (e.g., duration, level) in the context of perceptual judgments about musical dimensions: meter, harmony, melody, and timbre. In the meter test, the listener is asked to judge whether a tone sequence is duple or triple in meter. The harmony test requires that the listener make judgments about the stability of the chord sequences. In the melody test, the listener must judge whether a comparison melody is the same as a standard melody when presented in transposition and in the context of a chordal accompaniment that serves as a mask. The timbre test requires that the listener determines which of two comparison tones is different in timbre from a standard tone (ABX design). Twenty-one HI participants and 19 normal-hearing (NH) participants were recruited to carry out the music tests. Participants were tested twice on separate occasions to evaluate test-retest reliability. RESULTS: The HI group had significantly higher discrimination thresholds than the NH group in 7 of the 10 low-level physical dimensions: frequency discrimination in the meter test, dissonance and intonation perception in the harmony test, melody-to-chord ratio for both melody types in the melody test, and the perception of brightness and spectral irregularity in the timbre test. Small but significant improvement between test and retest was observed in three dimensions: frequency discrimination (meter test), dissonance (harmony test), and attack length (timbre test). All other dimensions did not show a session effect. Test-retest reliability was poor (<0.6) for spectral irregularity (timbre test); acceptable (>0.6) for pitch and duration (meter test), dissonance and intonation (harmony test), and melody-to-chord ratio I and II (melody test); and excellent (>0.8) for level (meter test) and attack (timbre test). CONCLUSION: The AMP test revealed differences in a wide range of music perceptual abilities between NH and HI listeners. The recognition of meter was more difficult for HI listeners when the listening task was based on frequency discrimination. The HI group was less sensitive to changes in harmony and had more difficulties with distinguishing melodies in a background of music. In addition, the thresholds to discriminate timbre were significantly higher for the HI group in brightness and spectral irregularity dimensions. The AMP test can be used as a research tool to further investigate music perception with hearing aids and compare the benefit of different music processing strategies for the HI listener. Future testing will involve larger samples with the inclusion of hearing aided conditions allowing for the establishment of norms so that the test might be appropriate for use in clinical practice.

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 score0.873
Threshold uncertainty score0.169

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 head0.081
GPT teacher head0.268
Teacher spread0.187 · 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

Citations37
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueEar and HearingSame topicNeuroscience and Music PerceptionFrench-language works237,207