Most existing datasets for sound event recognition (SER) are relatively small and/or domain-
specific, with the exception of AudioSet, based on over 2 M tracks from YouTube videos and …

The objectives of this work are cross-modal text-audio and audio-text retrieval, in which the
goal is to retrieve the audio content from a pool of candidates that best matches a given …

To reveal the importance of temporal precision in ground truth audio event labels, we
collected precise (∼ 0.1 sec resolution)" strong" labels for a portion of the AudioSet dataset …

There are lots of research papers for ASC, and in recent years it is rapidly increasing.
DCASE also provides different types of competition for the submission of several papers to …

We consider the task of retrieving audio using free-form natural language queries. To study
this problem, which has received limited attention in the existing literature, we introduce …

Self-supervised representation learning can mitigate the limitations in recognition tasks with
few manually labeled data but abundant unlabeled data—a common scenario in sound …

Facing the complex marine environment, it is extremely challenging to conduct underwater
acoustic target feature extraction and recognition using ship-radiated noise. In this paper …

In this paper, we study the performance of variants of well-known Convolutional Neural
Network (CNN) architectures on different audio tasks. We show that tuning the Receptive …

This paper addresses the problem of species classification in bird song recordings. The
massive amount of available field recordings of birds presents an opportunity to use …

The Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge
focuses on audio tagging, sound event detection and spatial localisation. DCASE 2019 …