Algorithmic Composition, illustrated by my own work: A review of the period 1971-2008
Bibliographic record
Abstract
Since 1971, marking my first departure from fourteen years of spontaneous composition, my work has been mainly algorithmic in nature. Some of it was generated by single algorithm sets developed for multiple use, the properties of the results deriving from the input. In other cases, the algorithms were used once only, with the dedicated purpose of generating a single work. The algorithms ranged from verbal instructions to complex computer programs. Of the eighty-odd pieces I have composed since 1971, about a quarter arose from three verbal scores, Textmusic (converting written text into notes),...until... (working systematically with interval ratios) and Relationships (working with levels of complexity of melody and rhythm in the context of harmony and meter). Another quarter or the pieces were generated by three individual computer programs- TXMS (Textmusic packaged into software), Autobusk (for the generation of MIDI pitch sequences from scales and meters as well as twelve real-time variable parameters such as tonal and metric field strength) and PAPAGEI (for the generation of MIDI events based on patchable live interaction with an improvising performer). Yet another quarter of my compositions since 1971 have resulted from dedicated sets of algorithms for one-time use. Further computer programs such as Synthrumentator and Spectasizer (for the conversion of speech sounds into instrumental scores) were used to generate parts of other compositions. In this paper I will refer to TXMS, Autobusk and Synthrumentator as well as to two compositions generated by dedicated software,...or a cherish'd bard... (in which the algorithms generate all aspects of the piece from pitch and rhythm to the overall form) and Approximating Pi (in which algebraically defined algorithms generate the sound waves). Textmusic In 1970, the music I composed derived strongly from serial techniques of composers from Schoenberg to Stockhausen,
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".