Music analysis and retrieval systems for audio 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
Abstract The constantly increasing amount of audio available in digital form necessitates the development of software systems for analyzing and retrieving digital audio. In this work, we describe our efforts in developing such systems. More specifically, we describe the design philosophy behind our approach, the specific problems we try to solve, and how we evaluate the performance of our algorithms. Automatic music analysis and retrieval of non‐speech digital audio is a relatively new field, and the existing techniques are far from perfect. To improve the performance of the developed techniques, two main techniques are used: (1) integration of information from multiple analysis and retrieval algorithms and (2) the use of graphical user interfaces that enable the user to provide feedback to the design, development, and evaluation of the algorithms. All the developed algorithms and user interfaces are integrated under MARSYAS, a software framework for research in computer audition.
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.
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 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it