Which Aural Skills are Necessary for Composing, Performing and Understanding Electroacoustic Music, and to what Extent are they Teachable by Traditional Aural Training?
Bibliographic record
Abstract
This paper reports a study that sought to discover the necessary aural skills for composing, performing, and understanding electroacoustic (EA) music and the extent of their teachability by traditional aural training according to an analysis of a mixed-method (qualitative/quantitative) questionnaire completed by a purposive sample of 15 experts in the field of electroacoustics. The participants evaluated a list of 50 potentially necessary aural skills, which were gathered from skills described in existing, but insufficiently applied, aural training systems and theoretical methods related to aural perception in EA, and provided additional skills they found necessary for EA. The survey revealed that the aural skills deemed the most necessary for EA by the participants were not regarded as sufficiently teachable by traditional aural training and the majority of the skills considered teachable by traditional aural training were not thought of as significantly necessary for the EA musician. Moreover, among the 50 skills listed in the questionnaire 56 per cent were deemed at least very necessary by the participants, with only 18 per cent of them viewed as sufficiently teachable by traditional aural training. The main implication of this study is a pressing need for further development, research, and experimental testing of aural training methods for EA.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".