About CRIM

Megaphone project


Summary: Megaphone was an interactive outdoor installation in Montréal's Quartier des spectacles (QDS), that allowed citizens to speak out publicly on a theme of their choice. Participants saw their words converted into giant visual art projected onto the front of the great UQAM Président-Kennedy building, thanks to Moment Factory imaging and CRIM speech recognition technologies. CRIM participated as a consultant for speech recognition in the project's initial phase, provided its technologies for real-time and offline recognition, and contributed to the integration of its technologies into the system installed at QDS.

Result: The installation final version included a live system, that projected words in real-time on a container located behind the speakers, and an offline system, which produced more accurate results that appeared 20 seconds later onto UQAM's building et were accumulated there as a long term memory of the spoken words. English and French were supported. Hundreds of participants used the installation between September 4 and November 4, 2013.

Partners: Moment Factory, National Film Board of Canada (NFB), Quartier des spectacles Montréal.

NFB video - A few candid moments at Megaphone

Teams

Upcoming event

  • DevOps - En rappel à Québec - Événement Les Affaires
    26 February 2020 8:30
    Hôtel Le Concorde Québec
    Le CRIM fier partenaire de la 2e édition de la conférence DevOps - En rappel à Québec, organisée par les Événement Les Affaires, qui se tiendra les 26 et 27 février 2020 | Hôtel le Concorde, Québec
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  • CANARIE RT @CANARIE_Inc: VESTA recueille les données des systèmes d'apprentissage en ligne quand les élèves lisent l'écran, sont distraits ou pense…
  • CRIM is happy to have contributed its expertise in #visual/#speech #analytics and operator-state #monitoring for th… https://t.co/JbAZ8mvm4O

Recent Publications

  • Validating BGP Update Using Blockchain-Based Infrastructure in Livre : Decentralised Internet of Things: A Blockchain Perspective

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  • Efficient Inference of Optimal Decision Trees

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