Research on sound recognition algorithm for home environment based on residual neural network

Mingde Zhou; Fei Xia; Xiang Zhao

doi:10.1117/12.3038069

7 August 2024 Research on sound recognition algorithm for home environment based on residual neural network

Mingde Zhou, Fei Xia, Xiang Zhao

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132292M (2024) https://doi.org/10.1117/12.3038069
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

This paper proposes a sound recognition algorithm for home environment based on deep learning. This algorithm uses the fusion feature extraction method to fuse the two feature extraction methods GFCC and FBank, and uses the fused feature value as the input of the ResNetSE network. Compared with related research, this method is optimized at the model input layer. By fusing two feature extraction methods, it replaces the traditional MFCC feature extraction method and improves the model's classification accuracy for home environment sounds. Experimental results show that the classification accuracy of the FBank-GFCC ResNetSE model proposed in this paper reached 97.4% on the Urbansound8K public data set. Compared with related work, the accuracy is improved by 6.4%. This shows that the algorithm has better performance in home environment sound classification tasks, and provides a more accurate and reliable solution for applications such as security monitoring in smart home systems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingde Zhou, Fei Xia, and Xiang Zhao "Research on sound recognition algorithm for home environment based on residual neural network", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132292M (7 August 2024); https://doi.org/10.1117/12.3038069

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KEYWORDS

Feature extraction

Feature fusion

Tunable filters

Detection and tracking algorithms

Neural networks

Evolutionary algorithms

Data modeling

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