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This paper presents a novel approach to acoustic scene classification (ASC) using a multi-branch three-dimensional convolutional neural network (3D-CNN) that captures spatial-temporal features from environmental sounds. The proposed model, termed Multi-Input 3D-SE-ResNet, incorporates advanced techniques such as discrete wavelet transformations and auxiliary supervised branches to enhance classification accuracy while mitigating overfitting. Evaluated on the DCASE 2019 dataset, the model demonstrates signif

Author

Pranjal Dey

Language

EN