Monday 19th November
| 9:00 | Registration & Coffee/Tea |
|
| 9:20 | Welcome |
Mark Plumbley |
| 9:30 | Keynote |
Stereophonic autoencoder on bio-transient to enhance biodiversity tracking, and other methods to monitor abyssea Hervé Glotin Université de Toulon, CNRS, LIS |
| 10:20 | Challenge spotlights |
Reports on the 5 tasks of the DCASE 2018 challenge |
| 10:50 | Coffee/Tea | |
| 11:20 | Oral session I |
Human and animal sound detection
L01
Acoustic event search with an onomatopoeic query: Measuring distance between onomatopoeic words and sounds
L02
Fast mosquito acoustic detection with field cup recordings: An initial investigation
L03
Acoustic bird detection with deep convolutional neural networks
L04
Domain tuning methods for bird audio detection
|
Learning from weakly-labeled data
Large-scale weakly labeled semi-supervised sound event detection in domestic environments
Romain Serizel, Nicolas Turpault, Hamid Eghbal-Zadeh, Ankit Parag Shah
Sound event detection using weakly labelled semi-supervised data with GCRNNs, VAT and self-adaptive label refinement
Robert Harb, Franz Pernkopf
Iterative knowledge distillation in R-CNNs for weakly-labeled semi-supervised sound event detection
Khaled Koutini, Hamid Eghbal-zadeh, Gerhard Widmer
Data-efficient weakly supervised learning for low-resource audio event detection using deep learning
Veronica Morfi, Dan Stowell
17:15 - 19:00 Welcome Reception
Tuesday 20th November
| 9:00 | Arrival & Coffee/Tea |
|
| 9:20 | Keynote |
Acoustic condition monitoring for smart city and industry environments Hanna Lukashevich Fraunhofer Institute for Digital Media Technology IDMT |
| 10:10 | Oral session III |
Acoustic scene classification
L09
Attention-based convolutional neural networks for acoustic scene classification
L10
Unsupervised adversarial domain adaptation for acoustic scene classification
|
Multi-label audio event classification
A multi-device dataset for urban acoustic scene classification
Annamaria Mesaros, Toni Heittola, Tuomas Virtanen
Training general-purpose audio tagging networks with noisy labels and iterative self-verification
Matthias Dorfer, Gerhard Widmer
Audio tagging system using densely connected convolutional networks
Il-Young Jeong, Hyungui Lim
General-purpose audio tagging by ensembling convolutional neural networks based on multiple features
Kevin Wilkinghoff
TBA