Bibliographic record
Abstract
He teaches courses in acoustics, speech science, instrumentation in audiology, and hearing aids.His research interests include speech communication, warning sound perception, hearing protection, and hearing loss prevention.He has authored over 150 journal articles, conference proceedings and book chapters.Professor Giguère is active in standards organisations and member of several national (CSA, ANSI) and international (ISO) technical workgroups on topics related to occupational hearing loss, hearing protection and audiology.He was president of the Canadian Acoustical Association (2007)(2008)(2009)(2010)(2011)(2012)(2013) Noise (2008-2014), and chair of the Technical Committee on Occupational hearing loss with the Canadian Standards Organisation (2016-2022).He is currently Associate Editor with The International Journal of Audiology and Member of the NHCA's Task Force on Auditory Situational Awareness.He is a Distinguished International Member of the Institute of Noise Control Engineering (INCE-USA). , co-chair of the International Commission on the International Commission on the Biological Effects ofThe keynote presented by Christian Giguère is entitled: Back-up alarms on heavy vehicles: We are moving forward! Audible back-up alarms are installed on heavy trucks and mobile equipment to alert workers and pedestrians of safety risks during reverse operations.Prompt reaction to back-up alarms by individuals nearby reversing vehicles depends on many acoustical (e.g., sound propagation pattern behind the vehicle) and psychoacoustical (e.g., perceived urgency and ability to localize alarm, effect of hearing loss and hearing protection) factors that are not comprehensively addressed in applicable standards such as SAE J994 and ISO 9533.This presentation will summarize results from a series of four collaborative studies conducted by the University of Ottawa and the IRSST in Montreal in the past twelve years.The focus was in comparing the relative benefits of two types of devices, the widely used tonal alarm and the emerging broadband alarm.Noureddine Atalla is a professor of Mechanical Engineering at Université de Sherbrooke.His core expertise is in computational vibro-acoustics and acoustic materials.He has authored over 200 papers in acoustics and vibration, encompassing a wide range of domains.His research includes the modeling of poroelastic and viscoelastic materials, the study of coupled fluid-structure problems, the investigation of the acoustic and dynamic response of sandwich and composite structures, as well as computational vibroacoustics.In addition to his scholarly articles, he has co-authored a book on the modeling of sound porous materials and another book on finite and boundary elements in structural acoustics and vibrations.Over the last decade, the author's team has explored various topics related to the modeling, characterization, and development of lightweight structures and their added sound packages, with a special focus on aircraft and aerospace applications.This talk will present a review of part of this body of research.In particular, the talk will demonstrate the effectiveness of employing the transfer matrix method, along with its numerous extensions, for accurately predicting the vibroacoustic response of a wide array of lightweight structures and noise control materials.The predictive capability of the method will also be illustrated for various types of excitations and systems with structured elements judiciously placed within the structure or the materials.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.253 | 0.084 |
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".