Given a short query with keyphrases about a research idea in 2019 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: A novel approach to perform unsupervised bioacoustic event detection by pooling the magnitudes of spectrogram frames

Keyphrases: unsupervised detection bioacoustic events audio

Dataset description:
TUT-SED Synthetic 2016: TUT-SED Synthetic 2016 contains of mixture signals artificially generated from isolated sound events samples. This approach is used to get more accurate onset and offset annotations than in dataset using recordings from real acoustic environments where the annotations are always subjective.
EPIC-KITCHENS-100: This paper introduces the pipeline to scale the largest dataset in egocentric vision EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras.
ESC-50: The ESC-50 dataset is a labeled collection of 2000 environmental audio recordings suitable for benchmarking methods of environmental sound classification. It comprises 2000 5s-clips of 50 different classes across natural, human and domestic sounds, again, drawn from Freesound.org.
BirdCLEF 2018: BirdClef 2018 is a bird soundscape dataset based on the contributions of the Xeno-canto network. The training set contains 36,496 recordings covering 1500 species of central and south America (the largest bioacoustic dataset in the literature).

Options:- A: TUT-SED Synthetic 2016  B: EPIC-KITCHENS-100  C: ESC-50  D: BirdCLEF 2018
ESC-50