The Timbre Toolbox: Extracting audio descriptors from musical signals
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
The analysis of musical signals to extract audio descriptors that can potentially characterize their timbre has been disparate and often too focused on a particular small set of sounds. The Timbre Toolbox provides a comprehensive set of descriptors that can be useful in perceptual research, as well as in music information retrieval and machine-learning approaches to content-based retrieval in large sound databases. Sound events are first analyzed in terms of various input representations (short-term Fourier transform, harmonic sinusoidal components, an auditory model based on the equivalent rectangular bandwidth concept, the energy envelope). A large number of audio descriptors are then derived from each of these representations to capture temporal, spectral, spectrotemporal, and energetic properties of the sound events. Some descriptors are global, providing a single value for the whole sound event, whereas others are time-varying. Robust descriptive statistics are used to characterize the time-varying descriptors. To examine the information redundancy across audio descriptors, correlational analysis followed by hierarchical clustering is performed. This analysis suggests ten classes of relatively independent audio descriptors, showing that the Timbre Toolbox is a multidimensional instrument for the measurement of the acoustical structure of complex sound signals.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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