  Most of the existing isolated sound event datasets comprise a small number of
sound event classes, usually 10 to 15, restricted to a small domain, such as
domestic and urban sound events. In this work, we introduce GISE-51, a dataset
spanning 51 isolated sound events belonging to a broad domain of event types.
We also release GISE-51-Mixtures, a dataset of 5-second soundscapes with
hard-labelled event boundaries synthesized from GISE-51 isolated sound events.
We conduct baseline sound event recognition (SER) experiments on the
GISE-51-Mixtures dataset, benchmarking prominent convolutional neural networks,
and models trained with the dataset demonstrate strong transfer learning
performance on existing audio recognition benchmarks. Together, GISE-51 and
GISE-51-Mixtures attempt to address some of the shortcomings of recent sound
event datasets, providing an open, reproducible benchmark for future research
along with the freedom to adapt the included isolated sound events for
domain-specific applications.
