Dynamic Networks of Sonic Interactions: An Interview with Agostino Di Scipio
Bibliographic record
Abstract
11 The Italian composer Agostino Di Scipio (see Figure 1) is one of the most interesting European personalities today working in the space between computer music and sound art. In his recent work, he creates purely sonic interactions between a source, realtime digital signal processors, and the room hosting the performance. The network of interactions is conceived as a dynamic, self-organizing system, symbiotically connected with the surrounding environment. The following interview addresses such issues and provides an overview of the theoretical and technological background behind them. It also touches on the central role of noise in Mr. Di Scipio’s live electronics compositions and on the degree of freedom allowed to human agents involved in the performance of such works. A list of his compositions is given in Table 1, and a list of recordings is provided in Table 2. Born in Naples in 1962, Mr. Di Scipio first approached composition as a self-taught musician, and later he pursued more formal studies at the Conservatory of L’Aquila and the University of Padua. A former visiting composer in several institutions, including Simon Fraser University (Burnaby, British Columbia, 1993) and the Sibelius Academy (Helsinki, 1995), he is today Professor of Electronic Music at the Conservatory of Naples and instructor in live electronics at Centre de Creation Musicale Iannis Xenakis (CCMIX) in Paris. In 2004, Mr. Di Scipio lectured at the University of Illinois, Urbana-Champaign, and at Johannes-GutenbergUniversitat in Mainz. A portrait compact disc, including some of the works recalled in the interview, will soon be released by Edition RZ, Berlin. He is also greatly interested in issues of music theory involving the relationship between art and technology, and he has published numerous articles and essays in international publications devoted to such issues. In 2004–2005, he lived in Berlin as a guest artist of
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".