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Record W2156141367 · doi:10.1109/ccece.2006.277564

An Online System for Synchronized Processing of Video and Audio Signals

2006· article· en· W2156141367 on OpenAlexaff
Mary Mikhail, Giovanni Palumbo, Jinane Mohammad, Mohamed El-Helaly, Aishy Amer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceAudio signalAudio signal processingSynchronization (alternating current)Process (computing)Speech recognitionAudio miningBottleneckVideo trackingAudio signal flowVideo processingVideo captureSIGNAL (programming language)Speech processingArtificial intelligenceSpeech codingComputer visionVoice activity detectionEmbedded systemTelecommunications

Abstract

fetched live from OpenAlex

For many audio-visual applications, the integration and synchronization of audio and video signals is essential. The objective of this paper is to develop a system that displays the active objects in the captured video signal, integrated with their respective audio signals in the form of text. The video and audio signals are captured and processed separately. The signals are buffered and integrated and synchronized using a time-stamping technique. Time-stamps provide the timing information for each of the audio and video processes, the speech recognition and the object detection, respectively. This information is necessary to correlate the audio packets to the video frames. Hence, integration is achieved without the use of video information, such as lip movements. The results obtained are based on a specific implementation of the speech recognition module, which is determined to be the bottleneck process in the proposed system

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.554
Threshold uncertainty score0.321

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 communication0.0000.001
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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2006
Admission routes1
Has abstractyes

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