Evaluating Sound Similarity Metrics for Differentiable, Iterative Sound-Matching
Why this work is in the frame
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Bibliographic record
Abstract
Manual sound design with a synthesizer is inherently iterative: an artist compares the synthesized output to a mental target, adjusts parameters, and repeats until satisfied. Iterative sound-matching automates this workflow by continually programming a synthesizer under the guidance of a loss function (or similarity measure) towards a target sound. Prior comparisons of loss functions have typically favored one metric over another, but only within narrow settings: limited synthesis methods, few loss types, often without blind listening tests. This leaves open the question of whether a universally optimal loss exists, or the choice of loss remains a creative decision conditioned on the synthesis method and the sound designer's preference. We propose differentiable iterative sound-matching as the natural extension of the available literature, since it combines the manual approach to sound design with modern advances in machine learning. To analyze the variability of loss function performance across synthesizers, we implemented a mix of four novel and established differentiable loss functions, and paired them with differentiable subtractive, additive, and AM synthesizers. For each of the sixteen synthesizer–loss combinations, we ran 300 randomized sound-matching trials. Performance was measured using parameter differences, spectrogram-distance metrics, and manually assigned listening scores. We observed a moderate level of consistency among the three performance measures. Our post hoc analysis shows that the loss function performance is highly dependent on the synthesizer. These findings underscore the value of expanding the scope of sound-matching experiments, and developing new similarity metrics tailored to specific synthesis techniques, rather than pursuing one-size-fits-all solutions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it