Towards Deconstructivist Music: Reconstruction paradoxes, neural networks, concatenative synthesis and automated orchestration in the creative process
Bibliographic record
Abstract
Since the 1980s, deconstruction has become a popular approach for designing architecture. In music, however, the term has not been absorbed as well by the related literature, with a few exceptions. In this article, ways to find ideological groundings for deconstructivism in music are introduced through the concepts of enchaînement and reconstruction paradoxes. Similar to the Banach–Tarski paradox in mathematics, reconstruction paradoxes occur when reconstructing the parts of a whole no longer yields the same properties as the whole. In music, a reconstruction paradox occurs when a piece constructed from tonal segments no longer yields a perceived tonality. Deconstruction in architecture heavily relies on computer-aided design (CAD) to realise complex ideas. Similarly in music, computer-aided composition (CAC) techniques such as neural networks, concatenative synthesis and automated orchestration are used. In this article, we discuss such tools in the context of this advocated new aesthetics: deconstructivist music .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".