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Record W2885690585 · doi:10.1145/3230744.3230752

Creative use of signal processing and MARF in ISSv2 and beyond

2018· article· en· W2885690585 on OpenAlexafffund
Zihao Song, Serguei A. Mokhov, Miao Song, Sudhir P. Mudur

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceToolboxModular designAudio signal processingGestureMotion captureSoftwareVideo processingMotion (physics)Signal processingImage processingMatch movingDigital signal processingHuman–computer interactionComputer graphics (images)MultimediaComputer visionAudio signalImage (mathematics)Computer hardware

Abstract

fetched live from OpenAlex

Illimitable Space System (ISS) is a real-time interactive configurable toolbox for use by artists to create interactive visual effects in theatre performances and in documentaries through user inputs such as gestures and voice. Kinect has been the primary input device for motion and video data capture. In this work in addition to the existing motion based visual and geometric data processing facilities present in ISSv2, we describe our efforts to incorporate audio processing with the help of Modular Audio Recognition Framework (MARF). The combination of computer vision and audio processing to interpret both music and human motion to create imagery in real time is both artistically interesting and technically challenging. With these additional modules, ISSv2 can help interactive performance authoring that employs visual tracking and signal processing in order to create trackable human-shaped animations in real time. These new modules are incorporated into the Processing software sketchbook and language framework used by ISSv2. We verify the effects of these modules, through live demonstrations which are briefly described.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.233
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes2
Has abstractyes

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