Benchmark for Kitchen20, a daily life dataset for audio-based human action recognition

This paper introduces a new raw-audio, environmental, kitchen-related, non-vocal dataset to fill what we consider as a gap in the context of audio datasets. Our so-called Kitchen20 dataset is compared to other datasets such as ESC-50 and shown that both datasets can be merged together in what we call ESC-70. A human quantitative appreciation of the audio samples contained in Kitchen20 is provided as well as several machine learning benchmarks on both Kitchen20 and ESC-70.