AGS: An Dataset and Taxonomy for Domestic Scene Sound Event Recognition

Nan Che, Chenrui Liu, Fei Yu
8/30/2023
cs.SDcs.AIeess.AS

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Abstract

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 .

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Cite this paper

@article{che2023ags,
  title  = {AGS: An Dataset and Taxonomy for Domestic Scene Sound Event Recognition},
  author = {Nan Che and Chenrui Liu and Fei Yu},
  year   = {2023},
  eprint = {2308.15726},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.15726v1}
}

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