Automatic detection of head voice in sung musical signals via machine learning classification of time-varying partial intensities
Bibliographic record
Abstract
The automatic detection of portions of a musical signal produced according to time-varying performance parameters is an important problem in musical signal processing. The present work attempts such a task: the algorithms presented seek to determine from a sung input signal which portions of the signal are sung using the head voice, also known as falsetto in the case of a male singer. In the authors’ prior work [Mysore et al., Asilomar Conf. Signal. Sys. Comp. (2006) (submitted)], a machine learning technique known as a support vector classifier [Boyd and Vandenberghe, 2004] was used to identify falsetto portions of a sung signal using the mel-frequency cepstral coefficients (MFCCs) of that signal (computed at a frame rate of 50 Hz). In the present work, the time-varying amplitudes of the first four harmonics, relative to the intensity of the fundamental, and as estimated by the quadratically interpolated fast Fourier transform (QIFFT) [Abe and Smith, ICASSP 2005], are used as a basis for classification. Preliminary experiments show a successful classification rate of over 95% for the QIFFT-based technique, compared to approximately 90% success with the prior MFCC-based approach. [Ryan J. Cassidy supported by the Natural Sciences and Engineering Research Council of Canada.]
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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 | 0.000 |
| Open science | 0.001 | 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".