Acoustic scene classification based on three-dimensional multi-channel feature-correlated deep learning networks

As an effective approach to perceive environments, acoustic scene classification (ASC) has received considerable attention in the past few years. Generally, ASC is deemed a challenging task due to subtle differences between various classes of environmental sounds. In this paper, we propose a novel a...

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Bibliographic Details
Published in:Scientific reports 2022-08, Vol.12 (1), p.13730-13730, Article 13730
Main Authors: Qu, Yuanyuan, Li, Xuesheng, Qin, Zhiliang, Lu, Qidong
Format: Article
Language:English
Subjects:
639/166/987
704/172/4081
Acoustics
Classification
Deep learning
Humanities and Social Sciences
multidisciplinary
Neural networks
Science
Science (multidisciplinary)
Temporal variations
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Summary:As an effective approach to perceive environments, acoustic scene classification (ASC) has received considerable attention in the past few years. Generally, ASC is deemed a challenging task due to subtle differences between various classes of environmental sounds. In this paper, we propose a novel approach to perform accurate classification based on the aggregation of spatial–temporal features extracted from a multi-branch three-dimensional (3D) convolution neural network (CNN) model. The novelties of this paper are as follows. First, we form multiple frequency-domain representations of signals by fully utilizing expert knowledge on acoustics and discrete wavelet transformations (DWT). Secondly, we propose a novel 3D CNN architecture featuring residual connections and squeeze-and-excitation attentions (3D-SE-ResNet) to effectively capture both long-term and short-term correlations inherent in environmental sounds. Thirdly, an auxiliary supervised branch based on the chromatogram of the original signal is incorporated in the proposed architecture to alleviate overfitting risks by providing supplementary information to the model. The performance of the proposed multi-input multi-feature 3D-CNN architecture is numerically evaluated on a typical large-scale dataset in the 2019 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2019) and is shown to obtain noticeable performance gains over the state-of-the-art methods in the literature.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-17863-z