Acoustic Scene Classification based on Sound Textures and Events

Jiaxing Ye, Takumi Kobayashi, Masahiro Murakawa, Tetsuya Higuchi

Abstract: Semantic labelling of acoustic scenes has recently emerged as active topic covering a wide range of applications, e.g. surveillance and audio-based information retrieval. In this paper, we present an effective approach for acoustic scene classification through characterizing both background sound textures and acoustic events. The work takes inspiration from the psychoacoustic definition of acoustic scenes, that is, "skeleton of (acoustic) events on a bed of (sound) texture". In detail, we firstly employ distinct models to exploit sound textures and events in acoustic scenes, individually. Subsequently, based on fact that the perceptual importance of two parts will vary with respect to different scene categories, we develop favourable class-conditional fusion scheme to aggregate two-channel information. To validate proposed approach, we conduct extensive experiments on Rouen dataset which includes 19 categories of daily acoustic scenes with 3026 real-world recordings, and the proposed approach outperforms state-of-the-art methods by a large margin.