Research on Acoustic Scene Classification Based on Multiple Mixed Convolutional Neural Networks
Lidong Yang, Zhuangzhuang Zhang, Jiangtao Hu · 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) · 2019
In order to solve the problem of complex feature extraction and low classification accuracy of traditional acoustic scene classification (ASC) model, this paper proposes a hybrid ASC model based on convolutional neural network (CNN) and long-short-time memory network (LSTM). The convolutional layer in the CNN learns the invariant features from the time-frequency input, the extracted features are input to the LSTM for training, the LSTM units to process the sequence of the extracted features by CNNs. The model finally outputs the result through the classifier. We evaluate the model on UrbanSound8k dataset and analyze which type of the model is more suitable for ASC. The experimental results show that compared with the traditional neural network classification model, the proposed hybrid neural network classification algorithm has better classification accuracy.