A Noise-Robust Fft-Based Spectrum for Audio Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, an early auditory model (K. Wang and S. Shamma, 1994) that calculates a so-called auditory spectrum, has been employed in audio classification where excellent performance is reported along with robustness in noisy environment. Unfortunately, this early auditory model is characterized by high computational requirements and the use of nonlinear processing. In this paper, inspired by the inherent self-normalization property of the early auditory model, we propose a simplified FFT-based spectrum which is noise-robust in audio classification. To evaluate the comparative performance of the proposed FFT-based spectrum, a three-class (i.e., speech, music and noise) audio classification task is carried out wherein a support vector machine (SVM) is employed as the classifier. Compared to a conventional FFT-based spectrum, both the original auditory spectrum and the proposed self-normalized FFT-based spectrum show more robust performance in noisy test cases. Test results also indicate that the performance of the self-normalized FFT-based spectrum is close to that of the original auditory spectrum, while its computational complexity is significantly lower
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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 | 0.000 | 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 | 0.000 |
| 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 it