The Use of Sonification for the Analysis and Teaching of Interpretive Auditory Nuances
Bibliographic record
Abstract
The integrative powers of human auditory perception in masterful performing artists can create nuances of sound from the interpretation of notes on a written page that often seem beyond one's grasp as a listener. It is important to consider what type of feedback can provide a clearer understanding of nuances in sound to guide motor learning for the acquisition of new skills for expression. Master pianists' performance has been used as a model for imitation. However, to ensure the accuracy of imitation and clear understanding, sonification was examined for its effectiveness in providing a more immediate understanding of individual interpretations in terms of inter-onset timing and velocity of the notes. Three master concert pianists volunteered to record a performance of the Chopin Nocturne Opus 15 No. 1 on a Yamaha Disklavier Pro MIDI (music instrument digital interface) grand piano. Logic software was used to analyze and compare MIDI data from each performance from the perspective of phrase-by-phrase, note onset timings, and corresponding data from the other pianists. The study objectives were to examine commonalities and differences in timing and dynamics among the performances using MIDI measurements; to probe whether listening to comparative performance data assisted with feedback from sonification would enhance music students' understanding of the interpretive nuances through imitation; and to determine whether auditory-assisted sonification feedback could be used as a tool to expand students' interpretive choices and enhance performance. Participants imitated selected phrases of each of the master pianists, first with only music listening, and then with feedback from the sonified, comparative performance data. Results showed limited success in attempts to imitate the model with auditory feedback alone. Auditory-assisted sonification feedback significantly enhanced the participants' abilities to imitate the model faster and with greater accuracy in the final imitation experiments. The data gathered by the study provide insights on this kind of sonification as an effective feedback tool for heightening participants' auditory understanding of nuances in sound, as well as providing an effective teaching tool for imitation exercises.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".