Bibliographic record
Abstract
In this paper, we present our design of MusicScore, a professional grade application on the iOS platform for music composition and live performance tracking, used by composers and amateurs alike. As its foundation, we have designed and implemented a high-quality music engraver, capable of real-time interactive rendering on mobile devices, as well as an intuitive user interface based on multi-touch, both built from scratch using Objective-C and Cocoa Touch. To make MusicScore appealing to the general population for their practice and fun, we have introduced a unique auditory capability to MusicScore, so that it can "listen" to and analyze live instrument performance in real time. In order to compensate for the imperfect audio sensing system on mobile devices, we have proposed a collaborative sensing solution to better capture music signals in real time. To maximize the accuracy of live progress tracking and performance evaluation using a mobile device, we have designed a collection of note detection and tempo-based note matching algorithms, using a combination of microphone and accelerometer sensors. Based on our real-world implementation of MusicScore, extensive evaluation results show that MusicScore can achieve acceptably low error ratios, even for music pieces performed by highly inexperienced players.
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 | 0.000 | 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 | 0.001 |
| 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 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".