Auditory Perception of Self-Similarity in Water Sounds
Bibliographic record
Abstract
Many natural signals, including environmental sounds, exhibit scale-invariant statistics: their structure is repeated at multiple scales. Such scale-invariance has been identified separately across spectral and temporal correlations of natural sounds (Clarke and Voss, 1975; Attias and Schreiner, 1997; Escabi et al., 2003; Singh and Theunissen, 2003). Yet the role of scale-invariance across overall spectro-temporal structure of the sound has not been explored directly in auditory perception. Here, we identify that the acoustic waveform from the recording of running water is a self-similar fractal, exhibiting scale-invariance not only within spectral channels, but also across the full spectral bandwidth. The auditory perception of the water sound did not change with its scale. We tested the role of scale-invariance in perception by using an artificial sound, which could be rendered scale-invariant. We generated a random chirp stimulus: an auditory signal controlled by two parameters, Q, controlling the relative, and r, controlling the absolute, temporal structure of the sound. Imposing scale-invariant statistics on the artificial sound was required for its perception as natural and water-like. Further, Q had to be restricted to a specific range for the sound to be perceived as natural. To detect self-similarity in the water sound, and identify Q, the auditory system needs to process the temporal dynamics of the waveform across spectral bands in terms of the number of cycles, rather than absolute timing. We propose a two-stage neural model implementing this computation. This computation may be carried out by circuits of neurons in the auditory cortex. The set of auditory stimuli developed in this study are particularly suitable for measurements of response properties of neurons in the auditory pathway, allowing for quantification of the effects of varying the statistics of the spectro-temporal statistical structure of the stimulus.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".