Bibliographic record
Abstract
The playing of a musical instrument is one of the most skilled and complex interactions between a human and an artifact. Professional musicians spend a significant part of their lives initially learning their instruments and then perfecting their skills. The production, distribution and consumption of music has been profoundly transformed by digital technology. Today music is recorded and mixed using computers, distributed through online stores and streaming services, and heard on smartphones and portable music players. Computers have also been used to synthesize new sounds, generate music, and even create sound acoustically in the field of music robotics. Despite all these advances the way musicians interact with computers has remained relatively unchanged in the last 20-30 years. Most interaction with computers in the context of music making still occurs either using the standard mouse/keyboard/screen interaction that everyone is familiar with, or using special digital musical instruments and controllers such as keyboards, synthesizers and drum machines. The string, woodwind, and brass families of instruments do not have widely available digital counterparts and in the few cases that they do the digital version is nowhere as expressive as the acoustic one. It is possible to retrofit and augment existing acoustic instruments with digital sensors in order to create what are termed hyper-instruments. These hyper-instruments allow musicians to interact naturally with their instrument as they are accustomed to, while at the same time transmitting information about what they are playing to computing systems. This approach requires significant alterations to the acoustic instrument which is something many musicians are hesitant to do. In addition, hyper-instruments are typically one of a kind research prototypes making their wider adoption practically impossible. In the past few years researchers have started exploring the use of non-invasive and minimally invasive sensing technologies that address these two limitations by allowing acoustic instruments to be used without any modifications directly as digital controllers. This enables natural human-computer interaction with all the rich and delicate control of acoustic instruments, while retaining the wide array of possibilities that digital technology can provide. In this chapter, an overview of these efforts will be provided followed by some more detailed case studies from research that has been conducted by the author's group. This natural interaction blurs the boundaries between the virtual and physical world which is something that will increasingly happen in other aspects of human-computer interaction in addition to music. It also opens up new possibilities for computer-assisted music tutoring, cyber-physical ensembles, and assistive music technologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".