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Record W182689536 · doi:10.1007/s10514-012-9316-x

The ManyEars open framework

2013· article· en· W182689536 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAutonomous Robots · 2013
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsComputer scienceMicrophoneModular designInterface (matter)SoftwareMicrophone arraySound cardRobotGraphical user interfaceEmbedded systemHuman–computer interactionComputer hardwareOperating systemArtificial intelligenceTelecommunicationsSound pressure

Abstract

fetched live from OpenAlex

ManyEars is an open framework for microphone array-based audio processing. It consists of a sound source localization, tracking and separation system that can provide an enhanced speaker signal for improved speech and sound recognition in real-world settings. ManyEars software framework is composed of a portable and modular C library, along with a graphical user interface for tuning the parameters and for real-time monitoring. This paper presents the integration of the ManyEars Library with Willow Garage’s Robot Operating System. To facilitate the use of ManyEars on various robotic platforms, the paper also introduces the customized microphone board and sound card distributed as an open hardware solution for implementation of robotic audition systems.

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.

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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.999

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 communication0.0020.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.015
GPT teacher head0.262
Teacher spread0.247 · 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