An Improved Implementation for an Auditory-Inspired FFT Model with Application in Audio Classification
Bibliographic record
Abstract
In this paper, we present an improved implementation for an auditory-inspired FFT-based model which calculates a noise-robust FFT spectrum. Through the use of characteristic frequency (CF) values of the cochlear filters in an early auditory (EA) model for power spectrum selection, and the use of a pair of running averages for the implementation of self-normalization, the proposed FFT model allows more flexibility in the extraction of audio features. To evaluate the performance of the proposed FFT model, a speech/music/noise classification task is carried out wherein a decision tree learning algorithm (C4.5) is used as the classifier. Audio features used for classification include the mel-frequency cepstral coefficient (MFCC) features, a set of conventional spectral features, and spectral features calculated using the proposed FFT model. Compared to the conventional MFCC and spectral features, the spectral features based on the proposed FFT model show more robust performance in noisy test cases.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".