Hierarchical classification for acoustic scenes using deep learning

Acoustic Scene Classification (ASC) aims to obtain the sound environment by analyzing audio signals. Due to the low complexity and acquisition cost of audio signals, ASC has enormous potential in various applications, such as audio-based surveillance, smart cities/homes, and robotics. Recently, various methods have been proposed for ASC and achieved good performance. However, when they are used to address complex ASC problems, most of them suffer from the low-performance problem. In this paper, we propose to use hierarchical classification methods to replace the conventional flat approach in ASC applications, which utilizes the class hierarchy to optimize classification performance. In particular, we investigate the ASC problem under the framework of hierarchical classification. Firstly, to improve classification performance, three hierarchical classification methods introducing the class hierarchy of acoustic scenes are proposed for ASC. Moreover, to fully utilize the class hierarchy, a hybrid hierarchical classification method, and an optimal late fusion-based hierarchical method are proposed, which are based on the flexibility and simplification of hierarchical classification. The experiments demonstrate the efficacy of hierarchical ASC systems for performance improvement, and the best system achieves an accuracy of 78.86% on the DCASE 2020 Task1A dataset, resulting in accuracy gains of 24.76% and 8.52% absolute over the DCASE 2020 Task 1A baseline and the conventional non-hierarchical method, respectively.