Hybridization of Fractional Fourier Transform and Acoustic Features for Musical Instrument Recognition
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents musical instrument recognition for isolated music sound signals using hybridization of fractional fourier transform (FRFT) based features with timbrel (acoustic) features using feed forward neural network. The FRFT based features which is named as fractional MFCC are computed by replacing conventional discrete fourier transform in mel frequency cepstral coefficient (MFCC) with discrete FRFT. Hybrid features are obtained by effectively combining Fractional MFCC with timbrel features such as temporal, spectral and cepstral features. Feed forward neural network with back propagation algorithm has been used to test the performance of system and results were compared in terms of recognition accuracy and number of features. Proposed feature out performs over individual and other traditional features proposed in the literature. The experimentation is performed on isolated musical sounds of 19 musical instruments covering four different instrument families. The system is tested on benchmarked McGill University musical sound database.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| 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