The control of acoustic intensity during jazz and free improvisation performance
Bibliographic record
Abstract
The paper demonstrates the predominance of a pattern of acoustic intensity change in recorded improvisations in which intensity rises are shorter than falls and in which the rate of intensity change is greater in the rises than in the falls. A wide range of Western improvised music is studied, and the analyses are conducted by measuring intensity in moving windows across each piece. The windows used are 0.04 sec, 0.5 sec, 5 sec and 10 sec, chosen to sample a range of important musical structures such as patterns, phrases and phrase groups. In addition, a comparative analysis using detected rhythmic beats as the window, with slightly variable lengths, is presented. The recurrent pattern is interpreted in terms of a hypothesised Force-Effort-Energy-Loudness-Affect chain, linking performer (or composer) with listeners and with other performers. Partial experimental investigation of this chain in other work has been consistent with the theory in supporting a major role of acoustic intensity in the perception of both musical change and affect. It seems that improvisers share this patterning of acoustic intensity with interpreters of classical music, and composers of electroacoustic music. Thus we suggest that music made without acoustic instruments, that is without the physical intervention of performers providing the energy to activate a sounding instrument, has developed the same pattern because composers recognise its expressive power as a statistical archetype. It remains to be seen whether this statistical feature could have been assimilated from environmental and/or speech sounds.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".