Using spiking onset neurons and a recurrent neural network for sound identification.
Bibliographic record
Abstract
Physiological evidence suggests that specific neurons within the cochlear nucleus specialize in sound onset detection. These are innervated by type 1 spiral ganglion fibers covering a relatively wide spectrum. Sudden increases in sound energy (e.g., during the initial portion of a sound) result in an increased firing rate in a downstream onset neuron. Onset timing and spectral location are thought to play a role both in auditory stream separation and sound identification and interpretation. Onset neurons are modeled using leaky integrate-and-fire units innervated by spiking data streams produced using a passive gammatone filterbank followed by positive-going zero-crossing detection. Signal level is coded using multiple spike trains per filter channel. The model is presented with a succession of 607 musical samples selected from the McGill dataset and the pattern of onset spikes recorded for each sound. Groups of onset spikes occur close to the beginning of each note. The objective is to use the pattern of spikes, produced by the onset neuron model, as a fingerprint of the original acoustic signal. These onset fingerprints are presented to a recurrent neural network (reservoir network) to attempt to classify them. The results are compared with a sound classification scheme based on cepstral coefficients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".