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

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.