Seeing Sound: A tool for teaching music perception principles
Bibliographic record
Abstract
One challenge we have found in teaching auditory perception is the difficulty of conveying the combined spectral and temporal complexity of natural musical sounds. Even single tones from musical instruments produce sounds that can only be fully conveyed using some type of 3D display showing continual changes in (1) amplitude and in (2) spectrum continuously over (3) time. It can be difficult to convey in 2D textbook figures, obfuscating the importance of dynamic temporal changes in acoustic structure and auditory perception. This pedagogical challenge mirrors a larger issue in auditory perception research, where the role of temporal changes in sounds often goes overlooked. Our aim with this project was to create a software tool offering an intuitive framework for understanding important concepts related to timbre perception useful for students with a background in either music or the sciences. To this end, we designed a program allowing students to explore and manipulate complex, time-varying sounds. Our software uses an intuitive Graphical User Interface (GUI) to covey the role of temporal changes in amplitude. It can also be used to generate stimuli for perceptual experiments, facilitating better use of complex, time-varying sounds in the exploration of auditory perception. A beta version of this tool was used in a 70-student introductory course during the 2016-2017 academic year, and we are also exploring potential research applications for the tool in our lab. A beta version of this software is freely available to our colleagues and other interested parties at https://maplelab.net/pedagogy/
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.017 |
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".