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 …

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 …

This paper introduces Task 2 of the DCASE2019 Challenge, titled" Audio tagging with noisy
labels and minimal supervision". This task was hosted on the Kaggle platform as" Freesound …

The study of label noise in sound event recognition has recently gained attention with the
advent of larger and noisier datasets. This work addresses the problem of missing labels …

Real-world sound scenes consist of time-varying collections of sound sources, each
generating characteristic sound events that are mixed together in audio recordings. The …

Recent studies have put into question the commonly assumed shift invariance property of
convolutional networks, showing that small shifts in the input can affect the output …

The development of a sound event detection (SED) system is no trivial task where one has
to consider both audio tagging and temporal localization concurrently. Often model …

This work describes and discusses an algorithm submitted to the Sound Event Localization
and Detection Task of DCASE2019 Challenge. The proposed methodology relies on …

Audio classification and other fields of pattern recognition have developed at an astounding
pace due to advances in machine learning. The availability of training data, especially …

Deep learning models have recently led to significant improvements in a wide variety of
tasks. Known as being a very powerful tool capable of generalizing better than traditional …