Imagine standing on a street corner in the city. With your eyes closed you can hear and
recognize a succession of sounds: cars passing by, people speaking, their footsteps when …

Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer
applications, as they prove to be both autonomous and flexible in a variety of environments …

The advancement of audio-language (AL) multimodal learning tasks has been significant in
recent years, yet the limited size of existing audio-language datasets poses challenges for …

Acoustic data provide scientific and engineering insights in fields ranging from biology and
communications to ocean and Earth science. We survey the recent advances and …

This paper introduces the acoustic scene classification task of DCASE 2018 Challenge and
the TUT Urban Acoustic Scenes 2018 dataset provided for the task, and evaluates the …

We learn rich natural sound representations by capitalizing on large amounts of unlabeled
sound data collected in the wild. We leverage the natural synchronization between vision …

DCASE 2017 Challenge consists of four tasks: acoustic scene classification, detection of
rare sound events, sound event detection in real-life audio, and large-scale weakly …

One of the obstacles in research activities concentrating on environmental sound
classification is the scarcity of suitable and publicly available datasets. This paper tries to …

This paper evaluates the potential of convolutional neural networks in classifying short audio
clips of environmental sounds. A deep model consisting of 2 convolutional layers with max …

Public evaluation campaigns and datasets promote active development in target research
areas, allowing direct comparison of algorithms. The second edition of the challenge on …