Swarm modulation: An algorithm for real-time spectral transformation
Bibliographic record
Abstract
A novel class of modulation is introduced, for frequency transformation and spectral synthesis in real-time. Swarm modulation has potential applications to enhance human hearing with extended frequency ranges, in medical diagnostics for electrocardiogram (ECG), electroencephalogram (EEG) and other medical signals, for RADAR analysis, for user-interface sonification, and for sound synthesis or non-synthetic sound transformation. Swarm modulation is a new way to transform signals, and is demonstrated for transforming subsonic and ultrasonic sound into the audible range of human hearing. Swarm modulation is based on the principle of phase-incoherent frequency-roaming oscillation. Features in the frequency-time plane are reconstructed via a time-varying process, controllable with instantaneous zero-latency reaction time to new information. Swarm modulation allows prioritization of salient output spectral features for efficient processing, and overcomes cyclic beating patterns when Fourier and wavelet-based methods are applied in a stationary manner. Swarm modulation can flexibly re-map sound when a user expressively touches physical matter creating vibration. By detecting subsonic, sonic and ultrasonic vibrations, we can add to materials a rich acoustic user-feedback that can be adjusted to sound like a bell, xylophone, dull piece of wood, or a variety of other objects, in real-time. By dynamically controlling the output sound spectrum depending on the input spectrum, simultaneously with a continuous and low-latency temporal response, the system imitates the physicality of touching a real object. Applied in control panels and expressive control surfaces, swarm modulation can create realistic sonic feedback, for human head-up operation of controls in critical applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".