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

TL;DR

This paper introduces the AGS dataset for domestic scene sound event recognition, addressing the lack of public data in this area, and evaluates advanced recognition methods on this new dataset.

Contribution

The paper provides a new comprehensive dataset for indoor environmental sound recognition and analyzes the performance of state-of-the-art methods on it.

Findings

The AGS dataset includes overlapping sounds and background noise.

Advanced recognition methods are evaluated on the dataset.

The dataset reveals new challenges in domestic sound event recognition.

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 .