A biophysical model of the human cochlea for speech stimulus using STFT
Bibliographic record
Abstract
This paper presents a new approach to an auditory model which matches closely the response patterns in physiological data for single tone inputs. Including the biophysical complexity of the wave motion in the cochlea and considering all terms of the motion equation, the new model can evaluate the auditory spectrum using the Basilar membrane (BM) displacement and the inner hair cells' (IHCs) firing rate for all input signals, including speech. We employ the partial differential motion equations of the BM and its parameters measured for the human auditory system, and design an algorithm which uses short time Fourier transform (STFT) to compute the output for speech stimulus. The idea is to isolate the input signal in the vicinity of a time-window and try to follow the changes in its frequencies and their influences on the signal perceived by the auditory system. The new model includes the nonlinearity action of outer hair cells (OHCs) and provides a new auditory spectrum for speech inputs in the real time domain which reflects a proper view of propagating signal in the cochlea. Despite most of the previous models this model can track the effects of high formant frequencies in the human cochlea as well. This model is a new signal processing tool for studying the response of the auditory system to transient signals which is highly demanded in various speech enhancement and audio coding algorithms
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".