Amazing Grace: How Sweet the Sound of Synthesised Bagpipes
Bibliographic record
Abstract
A bagpipe is a type of wind instrument that contains a melody pipe, which has an enclosed reed called the chanter and other drone pipes. The chanter is the part of the bagpipe that supplies the note, and the air that the pipes are fed is provided by the bag, which is inflated by a blowpipe and driven by the player’s arm. The goal of this project was to create a bagpipe using a program called Supercollider. Supercollider is used for audio synthesis. While creating this artificial bagpipe (here on referred to as a ‘synth’), it was broken down into four components: the chanter, the base drone, the first tenor drone and the second tenor drone. The chanter has the frequency of the note, the base drone’s frequency will be half that of the chanter and the frequency of the tenor drone will be half that of the base drone. This is because of the length of the pipes in relation to each other. In order to create the synth, a sine oscillator was used, and then put through a resonance filter, and then a reverb filter. This was done in order to mimic the echo that sound has when it is forced through a tube, or enclosed space. All four pipes were added together to create the synth. In order to play a song, the synth was put into a pattern so Supercollider could receive an array of notes, which serve as the frequency of the chanter, and then play the song automatically. The notes for Amazing Grace were transcribed into midi-notes and beat durations and these arrays were fed into the pattern to create the song. The synthetic version of Amazing Grace, in terms of frequency and loudness, was then graphed and compared to the graph of a recording of Amazing Grace played on a real bagpipe. There are differences between the two sound files, the most significant being that the real bagpipe has much more variation in terms of loudness. The synthesized bagpipe had a more gradual and subdued noise level, where the natural bagpipe was much more randomized. Taking the comparisons into consideration, Supercollider can be used to create an approximation of a bagpipe, but under scrutiny, the artificial version currently falls short.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".