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Conference papers

Early frame-based detection of acoustic scenes

Maxime Sangnier 1, 2 Jérôme Gauthier 1 Alain Rakotomamonjy 3 
1 LS2D - Laboratoire Sciences des Données et de la Décision
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
3 DocApp - LITIS - Equipe Apprentissage
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
Abstract : Let us consider a specific acoustic scene appearing in a continuous audio stream recorded while making a trip a in city. In this work, we aim at detecting at the earliest opportunity the several occurrences of this scene. The objective in early detection is then to build a decision function that is able to go off as soon as possible from the onset of a scene occurrence. This implies making a decision with an incomplete information. This paper proposes a novel framework in this area that i) can guarantee the decision made with a partial observation to be the same as the one with the full observation; ii) incorporates in a non-confusing manner the lack of knowledge about the minimal amount of information needed to make a decision. The proposed detector is based on mapping the temporal sequences to a landmarking space thanks to appropriately designed similarity functions. As a by-product, the built framework benefits from a scalable learning problem. A preliminary experimental study provides compelling results on a soundscape dataset.
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Submitted on : Monday, May 9, 2022 - 4:40:42 PM
Last modification on : Wednesday, June 22, 2022 - 3:24:11 AM


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Maxime Sangnier, Jérôme Gauthier, Alain Rakotomamonjy. Early frame-based detection of acoustic scenes. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) 2015, Oct 2015, New Paltz, United States. pp.7336884, ⟨10.1109/WASPAA.2015.7336884⟩. ⟨hal-01890049⟩



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