Perception of timbre dimensions: Psychophysics and electrophysiology in humans.
Bibliographic record
Abstract
Timbre is a perceptual attribute that characterizes the identity of a sound source. Psychoacoustical experiments have revealed that this perceptual attribute is multidimensional, with timbre dimensions (such as sharpness or brightness) corresponding to a variety of acoustical parameters: attack time, spectral centroid, spectrum fine structure, spectral flux, etc. The question arises whether timbral dimensions are processed holistically or separately in the auditory system. This issue was addressed in a series of behavioral and event-related potential (ERP) studies in humans [Caclin et al., J. Acoust. Soc. Am. (2005); J. Cogn Neurosci. (2006); J. Cogn Neurosci. (2008); Brain Res. (2007)]. Different timbres were created by manipulating attack time, spectral centroid, and spectrum fine structure. Dissimilarity ratings confirmed that these three parameters were indeed major determinants of timbre. Their representation in auditory sensory memory was explored using the mismatch negativity (MMN) component of the auditory ERP. MMN results indicated mainly separate sensory memory representations of these dimensions in auditory cortex. Using Garner interference paradigm, evidence was found for an interactive processing of timbre dimensions at other levels of analysis than sensory memory, including early perceptual processing stages. These results can be synthesized in a model of timbre perception postulating separate channels of processing for different timbre dimensions, with cross-talk between those channels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".