A Flexible Tool for the Visualization and Manipulation of Musical Mapping Networks
Bibliographic record
Abstract
Most digital musical instruments (DMIs) gather gestural input from musicians by way of electronic sensors and transform these data into sound through separate synthesis engines.The mapping of control inputs to synthesis parameters is arbitrary, multi-faceted and extremely important for the effectiveness of DMIs.Software tools exist to aid in this process and attempt to render the task of musical mapping more transparent, swift and configurable.This thesis presents MapperGUI, a cross-platform graphical tool for the manipulation of musical mapping networks.The libmapper software library, developed at the Input Devices and Music Interaction Laboratory, creates a standard framework for DMIs to communicate data on a distributed network and map their signals collaboratively in real-time.MapperGUI presents a graphical user interface for libmapper networks, allowing non-expert users to manipulate the textbased system.The interface aims to be flexible, such that it can accommodate the vast array of musical networks and tasks that must be performed when mapping.To this end, it provides multiple independent visualizations and interaction modes within a single framework.This document explores some of the issues challenging the field of musical mapping and describes the motivations behind the MapperGUI project in this context.Relevant research in the fields of data visualization and interface design is summarized and applied to the task of creating a graphical user interface for libmapper networks.Prior graphical interfaces for libmapper are examined for successful features that can be incorporated into MapperGUI.Specific implementation challenges and features of the final program are described.Insight gained from interviews with users of MapperGUI is presented, along with future work and possible extensions for the interface.MapperGUI is available for free download as a standalone application at www.libmapper. org/downloads.All code is open-source and can be accessed at https://github.com/mysteryDate/webmapper.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".