pith. sign in

arxiv: 1807.10501 · v1 · pith:F5FXKGHPnew · submitted 2018-07-27 · 💻 cs.SD · eess.AS

Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments

classification 💻 cs.SD eess.AS
keywords dataeventeventslabeledtaskweaklyapplicationsboundaries
0
0 comments X
read the original abstract

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 .

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. HODGEPODGE: Sound event detection based on ensemble of semi-supervised learning methods

    cs.SD 2019-07 unverdicted novelty 3.0

    An ensemble of CRNNs trained with consistency regularization and MixUp on mixed labeled/unlabeled data reaches 42.0% event-based F-measure on DCASE 2019 Task 4, beating the 25.8% baseline.