  Most existing datasets for sound event recognition (SER) are relatively small
and/or domain-specific, with the exception of AudioSet, based on over 2M tracks
from YouTube videos and encompassing over 500 sound classes. However, AudioSet
is not an open dataset as its official release consists of pre-computed audio
features. Downloading the original audio tracks can be problematic due to
YouTube videos gradually disappearing and usage rights issues. To provide an
alternative benchmark dataset and thus foster SER research, we introduce
FSD50K, an open dataset containing over 51k audio clips totalling over 100h of
audio manually labeled using 200 classes drawn from the AudioSet Ontology. The
audio clips are licensed under Creative Commons licenses, making the dataset
freely distributable (including waveforms). We provide a detailed description
of the FSD50K creation process, tailored to the particularities of Freesound
data, including challenges encountered and solutions adopted. We include a
comprehensive dataset characterization along with discussion of limitations and
key factors to allow its audio-informed usage. Finally, we conduct sound event
classification experiments to provide baseline systems as well as insight on
the main factors to consider when splitting Freesound audio data for SER. Our
goal is to develop a dataset to be widely adopted by the community as a new
open benchmark for SER research.
