AGS: An Dataset and Taxonomy for Domestic Scene Sound Event Recognition
This work provides a dataset for researchers in audio surveillance, but it is incremental as it focuses on creating a new dataset rather than advancing methods.
The paper introduces AGS, a new dataset for domestic scene sound event recognition, addressing the lack of public datasets for indoor environmental sounds, and demonstrates its reliability by comparing advanced methods and analyzing challenges.
Environmental sound scene and sound event recognition is important for the recognition of suspicious events in indoor and outdoor environments (such as nurseries, smart homes, nursing homes, etc.) and is a fundamental task involved in many audio surveillance applications. In particular, there is no public common data set for the research field of sound event recognition for the data set of the indoor environmental sound scene. Therefore, this paper proposes a data set (called as AGS) for the home environment sound. This data set considers various types of overlapping audio in the scene, background noise. Moreover, based on the proposed data set, this paper compares and analyzes the advanced methods for sound event recognition, and then illustrates the reliability of the data set proposed in this paper, and studies the challenges raised by the new data set. Our proposed AGS and the source code of the corresponding baselines at https://github.com/taolunzu11/AGS .