Master 2018 2019
Stages de la spécialité SAR
Nouveau stage en deep learning et audio


Site : Advanced deep learning models for audio event detection in domestic environment
Lieu : Technicolor R&I Rennes
Encadrant : Ngoc Q. K. Duong quang-khanh-ngoc.duong@technicolor.com
Dates :Flexible, starting from February, March or April 2019 for 5-6 months
Rémunération :1200
Mots-clés : Parcours ATIAM : Acoustique, Parcours ATIAM : Informatique musicale, Parcours ATIAM : Traitement du signal

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Description

The internship addresses detection of audio events in domestic environment for emerging real life applications to be implemented within a set-top-box. This task, which has been benchmarked in DCASE challenges (see [1] for the DCASE 2018), has attracted a lot of attention in the past few years. With the advances in deep neural networks (DNN) and the release of large-scale audio datasets, numerous approaches have been investigated in the literature (see [2, 3] for a survey), including both supervised [3] and weakly-supervised [4] methods. Grounded on DCASE 2019 challenge with benchmarked datasets, the internship targets to build a state-of-the-art DNN model to do the inference accurately. Several settings might be considered : single channel vs. multichannel inputs, supervised vs. weakly supervised learning where the annotations are noisy and/or incomplete.

The intern will conduct both research and implemention while investigating the use of advanced DNN architectures and data augmentation strategies for the considered tasks. Depending on the actual work and the obtained result, the work may be concluded by a participation in the DCASE 2019 challenge and by a submission of a scientific publication in an international conference/workshop.

Bibliographie

[1] IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE), http://dcase.community/challenge2018/. [2] Shayan Gharib et al., “Acoustic scene classification : a competition review,” Aug. 2018. https://arxiv.org/pdf/1808.02357.pdf [3] Annamaria Mesaros et al., “Acoustic scene classification : an overview of DCASE 2017 challenge entries,”, Proc. IWAENC, 2018. http://www.cs.tut.fi/ mesaros/pubs/... [4] Romain Serizel et al., “Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments,” Proc. DCASE2018 Workshop, July 2018. https://hal.inria.fr/hal-01850270