Identification of musical instrument sounds under noise masking
Bibliographic record
Abstract
The functioning of timbre and other form-bearing dimensions in music depends on the acoustic generation of sound, as well as physiological responses and the psychological organization of the information carried by sound. Human hearing can be reshaped by individual experience and can even be affected by cultural circumstances. To understand how hearing loss can affect the perception of timbre, we adopted noise maskers of varying kinds into a one-interval 2AFC task measuring the performance of timbre identification. Stimuli were presented using participant's personal laptop over headphones/earphones. In each trial, either a 300-ms trumpet or clarinet note at 440-Hz fundamental (target) with the same pitch was embedded in the middle of a 1-second Gaussian noise (masker). Three types of noise were used: broad-band noise, low-pass noise filtered at 4 kHz, and band-pass noise filtered between 5 kHz and 9 kHz. The target was fixed at one sound level, while the maskers of each kind were attenuated to 3 different levels. Our preliminary results show that (1) performance on the timbre identification task can be undermined by increasing the masker level and (2) noise maskers through a low-pass filter are as effective as broadband noise maskers but not those through a band-pass filter, suggesting that lower frequencies contribute more to identification. In future studies, we will further refine the composition of the noise masker, in both spectral and temporal domains, to explore how acoustic features contribute to timbre perception.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".