libremidi: a cross-platform library for real-time MIDI 1 and 2
Bibliographic record
Abstract
MIDI (Musical Instrument Digital Interface) has been a fundamental backbone for communication between digital music devices in modern music production. With the recent release of the MIDI 2 standard, available on Linux and macOS and soon on Windows, there is a now need for a robust, efficient, and easy-to-use cross-platform MIDI 2 library. This paper introduces libremidi, a cross-platform C++ library stemming from RtMidi and ModernMidi, rewritten from the ground up for real-time MIDI 2 communication. libremidi provides a simple and consistent API for managing MIDI ports, including hot-plug support, and handling MIDI events. It supports multiple platforms, including Linux (through ALSA RawMidi, ALSA Sequencer, and PipeWire), Windows, macOS, iOS, FreeBSD, and JACK on all platforms, and abstracts the underlying platform-specific MIDI APIs into a unified interface while enabling the end-user to have precise control over the back-ends. Designed for applications that require real-time MIDI communication, such as music production software, digital audio workstations, and interactive installations, libremidi’s efficient and lowlevel API allows developers to build responsive and highperformance applications that can handle multiple MIDI inputs and outputs simultaneously. Work has also been done to approximate real-time guarantees by avoiding memory allocations altogether during input and output. This paper will provide an overview of libremidi’s architecture, API, features and improvements over the current cross-platform MIDI state of the art.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".