MLLab at WASPAA, DCASE and SANE 2019
In late October, Machine Listening Lab researchers will be participating at a series of back-to-back workshops in the United States focused on audio signal processing and computational scene analysis: the 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2019) taking place in New Paltz, NY, the 8th Speech and Audio in the Northeast workshop (SANE 2019) taking place in New York City, and the 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2019) taking place in New York City.
The following papers from MLLab members will be presented at WASPAA 2019:
- “City classification from multiple real-world sound scenes“, by Helen L. Bear, Toni Heittola, Annamaria Mesaros, Emmanouil Benetos, and Tuomas Virtanen
- “Polyphonic sound event and sound activity detection: a multi-task approach“, by Arjun Pankajakshan, Helen L. Bear, and Emmanouil Benetos
- “Investigating kernel shapes and skip connections for deep learning-based harmonic-percussive separation“, by Carlos Lordelo, Emmanouil Benetos, Simon Dixon, and Sven Ahlbäck
The following papers will be presented at DCASE 2019:
- “Robustness of Adversarial Attacks in Sound Event Classification“, by Vinod Subramanian, Emmanouil Benetos, and Mark B. Sandler
- “Onsets, activity, and events: a multi-task approach for polyphonic sound event modelling“, by Arjun Pankajakshan, Helen L. Bear, and Emmanouil Benetos
- “Audio tagging using a linear noise modelling layer“, by Shubhr Singh, Arjun Pankajakshan, and Emmanouil Benetos
Finally, the following posters will be presented at SANE 2019 (click here for poster abstracts):
- “An extensible cluster-graph taxonomy for open set sound scene analysis”, by Helen L. Bear and Emmanouil Benetos
- “Adversarial Attacks in Audio Applications”, by Vinod Subramanian, Emmanouil Benetos, and Mark B. Sandler
- “Neural Machine Transcription for Sound Events”, by Arjun Pankajakshan, Helen L. Bear, and Emmanouil Benetos
See you in New York state and city!