Bibliographic record
Abstract
The sustained increase in computing performance over the last decades has brought enough computing power to perform significant audio processing in affordable personal computers. Following this revolution, we have witnessed a series of improvements in sound transformation techniques and the introduction of numerous digital audio effects to modify effectively the time, pitch, and loudness dimensions of audio signals. Due to the complex and multi-dimensional nature of timbre however, it is significantly more difficult to achieve meaningful and convincing qualitative transformations. The tools currently available for timbre modifications (e.g. equalizers) do not operate along perceptually meaningful axes of singing voice timbre (e.g. breathiness, roughness, etc.) resulting in a transformation control problem. One of the goals of this work is to examine more intuitive procedures to achieve high-fidelity qualitative transformations explicitly controlling certain dimensions of singing voice timbre. Quantitative measurements (i.e. voice timbre descriptors) are introduced and used as high-level controls in an adaptive processing system dependent on the characteristics observed in the input signal. The transformation methods use a harmonic plus noise representation from which voice timbre descriptors are derived. This higher-level representation, closer to our perception of voice timbre, offers more intuitive controls over timbre transformations. The topics of parametric voice modeling and timbre descriptor computation are first introduced, followed by a study of the acoustical impacts of voice breathiness variations. A timbre transformation system operating specifically on the singing voice quality is then introduced with accompanying software implementations, including an example digital audio effect for the control and modification of the breathiness quality on normal voices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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 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".