Novel interfaces for audio manipulation
Bibliographic record
Abstract
In this work we investigate two novel methods for multi-parametric control of audio in the context of sound design and music creation.Both systems aim to improve user experience by leveraging mapping strategies from control input to synthesis parameters, extracting additional value from conventional and ubiquitous tools.A first engine uses landmark mapping of facial features and manipulation of basic, built-in plugins to effectively emulate spatial audio mapping using only hardware built into a standard laptop.A pilot experiment (N=4) shows that the system improves source location identification in users by 15% over baseline.We also propose an assistive engine for sound design, which leverages a simple statistical model based on expert domain presets to guide users in controlling a subtractive synthesizer.In a user study (N=11) the engine is shown to significantly improve user performance in a sound matching task (t(10) = 3.38, p = 0.01), boosting user performance by 0.89 standard deviations within-participant.We explain its effectiveness in terms of cognitive apprenticeship theory, where the engine, in offering suggestions in reaction to user movement in a tight feedback loop, acts as a mentor that exposes sound Abstract ii design thinking, and keeps the user in the zone of proximal development by making the task not too difficult nor too obvious.Taken together, these experiments prove that mapping alone can meaningfully augment the affordances of existing control and synthesis paradigms.iii Abrg Dans ce mmoire, nous tudions deux nouvelles mthodes de contrle multi-paramtrique de l'audio dans le contexte du design sonore et de la cration musicale.Les deux systmes visent amliorer l'exprience utilisateur en exploitant des stratgies de mappage entre les entres de contrle et les paramtres de synthse, extrayant ainsi une valeur ajoute d'outils conventionnels et omniprsents.Un premier moteur utilise le mappage de points de repre des caractristiques faciales et la manipulation de plugins intgrs de base pour muler efficacement le mappage audio spatial en utilisant uniquement le matriel intgr dans un ordinateur portable standard.Une exprience pilote (N = 4) montre que le systme amliore l'identification de la localisation des sources chez les utilisateurs de 15% par rapport la rfrence.Nous proposons galement un moteur d'assistance pour le design sonore, qui exploite un modle statistique simple bas sur des presets d'experts du domaine pour guider les utilisateurs dans le contrle d'un synthtiseur soustractif.Dans une tude utilisateur (N = 11), il est dmontr que le moteur amliore significativement les performances des utilisateurs dans une tche de correspondance sonore (t(10) = 3.38, Abrg iv p = 0.01), augmentant les performances des utilisateurs de 0.89 carts-types au sein des participants.Nous expliquons son efficacit en termes de thorie de l'apprentissage cognitif, o le moteur, en offrant des suggestions en raction au mouvement de l'utilisateur dans une boucle de rtroaction serre, agit comme un mentor qui expose la rflexion de design sonore et maintient l'utilisateur dans la zone de dveloppement proximal en rendant la tche ni trop difficile ni trop vidente.Ensemble, ces expriences prouvent que le mappage seul peut augmenter de manire significative les affordances des paradigmes de contrle et de synthse.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".