Discrimination Between Ascending/Descending Pitch Arpeggios
Bibliographic record
Abstract
Automatic music transcription can be defined as the analysis of the acoustic signal to extract a symbolic representation of music. Existing transcription systems typically consider just the notes played at a given moment; however, other aspects such as expressiveness and playing technique can also be considered. This work is focused on how chords are played. Specifically, we consider a special type of chords, those played in arpeggio style, or simply arpeggios, in which the notes are played fast, sequentially from the lowest to the highest pitched note or vice versa and with a large overlap of the notes' sound. The main goal of this paper is to determine the pitch direction in which the arpeggiated chord was played. Two different classification methods are considered: a Fisher linear discriminant and an SVM linear classification scheme. Different features are presented for this task: one is based on the Mel-frequency cepstral coefficients (MFCCs) and two others, specifically designed for this task, rely on different analyses of the spectrogram. Evaluations have been done with a wide number of musical instruments. The results show that the pitch direction can be reliably detected using the proposed methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".