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Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments
- Source :
- Workshop on Detection and Classification of Acoustic Scenes and Events, Workshop on Detection and Classification of Acoustic Scenes and Events, Nov 2018, Woking, United Kingdom
- Publication Year :
- 2018
- Publisher :
- HAL CCSD, 2018.
-
Abstract
- Submitted to DCASE2018 Workshop; International audience; This paper presents DCASE 2018 task 4. The task evaluates systems for the large-scale detection of sound events using weakly labeled data (without time boundaries). The target of the systems is to provide not only the event class but also the event time boundaries given that multiple events can be present in an audio recording. Another challenge of the task is to explore the possibility to exploit a large amount of unbalanced and unlabeled training data together with a small weakly labeled training set to improve system performance. The data are Youtube video excerpts from domestic context which have many applications such as ambient assisted living. The domain was chosen due to the scientific challenges (wide variety of sounds, time-localized events.. .) and potential industrial applications .
- Subjects :
- FOS: Computer and information sciences
Sound (cs.SD)
Sound event detection
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Audio and Speech Processing (eess.AS)
Semi-supervised learning
[INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD]
FOS: Electrical engineering, electronic engineering, information engineering
Weakly labeled data
Large scale
Computer Science - Sound
Electrical Engineering and Systems Science - Audio and Speech Processing
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Workshop on Detection and Classification of Acoustic Scenes and Events, Workshop on Detection and Classification of Acoustic Scenes and Events, Nov 2018, Woking, United Kingdom
- Accession number :
- edsair.doi.dedup.....5389fb32e83e14ce7865d221ab7b987c