Music and science meet at the micro level: Time-frequency methods and granular synthesis
Bibliographic record
Abstract
Musical research over the last century has become increasingly entwined with the areas of acoustics, psychoacoustics, and electroacoustics. One of the most striking results has been to push the frontiers of models of sound and music to the micro level, what is generally termed microsound. At this level, concepts of frequency and time are conjoined by a quantum relationship, with an uncertainty principle relating them that is precisely analogous to the more famous uncertainty principle of quantum physics. A class of methods of sound synthesis and signal processing known as time-frequency models have their basis at this quantum level such that changes in a signal’s time domain result in spectral alterations and vice versa. One such method, granular synthesis and the granulation of sampled sound, produces results by the generation of high densities of acoustical quanta called grains. Such a radical shift has profound implications for not only our models of sound design, but also for the compositional methods that emerge as well as the role of the composer in guiding complex processes. The paper will argue that these models are examples of a class of complex systems exhibiting emergent form that create a new form of virtual music instrument.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".