Audio analytics

Our team encompasses a set of skills dedicated to the extraction of information from an audio signal. 

An audio recording often contains more than one speaker. The process of diarisation partitions the audio signal into speaker segments, even when speaker are not known beforehand. Keyword detection consists in identifying the presence of certain words within audio recordings, whereas topic detection determines if a given subject has been discussed during a conversation. When the task is to accurately determine the position of each word or phoneme of a recording, we  speak of text/audio synchronization. We have also developed algorithms aimed at detecting a person’s emotions from an audio recording. It then becomes possible, in an automated system, to modify the system’s behavior according to the user’s reaction in order to adequately satisfy his interlocutor.

Related technologies: keyword detection, audio content indexing, emotion detection 

 

Teams

Releases

Recent news

  • CRIM Blog - Deep learning applied to graphs
    16/10/2018

    An article by Jade Guisiano, data science intern at CRIM.

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Upcoming event

  • ICTAC 2018
    19 October 2018 0:00
    Stellenbosch, Afrique du Sud
    CRIM will present a paper at the 15th International Colloquium on the Theoretical Aspects of Computing to take place 16-19 October 2018 in Stellenbosch, South Africa.
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Recent Publications

  • Towards Automatic Feature Extraction for Activity Recognition from Wearable Sensors: A Deep Learning Approach

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  • État des lieux des technologies web

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