LAUSR

Environmental sound classification (ESC) tasks are attracting more and more attention. Due to the complexity of the scene and personnel mobility, there are some difficulties in understanding and generating environmental sound models for ESC tasks. To address these key issues, this paper proposes an audio classification framework based on L-mHP features and the SE-ResNet50 model and improves a dual-channel data enhancement scheme based on a symmetric structure for model training. Firstly, this paper proposes the L-mHP feature to characterize environmental sound. The L-mHP feature is a three-channel feature consisting of a Log-Mel spectrogram, a harmonic spectrogram, and a percussive spectrogram. The harmonic spectrogram and percussive spectrogram can be obtained by harmonic percussive source separation (HPSS) of a Log-Mel spectrogram. Then, an improved audio classification model SE-ResNet50 is proposed based on the ResNet-50 model. In this paper, a dual-channel data enhancement scheme based on a symmetric structure is promoted, which not only makes the audio variants more diversified, but also makes the model focus on learning the time–frequency mode in the acoustic features during the training process, so as to improve the generalization performance of the model. Finally, the audio classification experiment of the framework is carried out on public datasets. An experimental accuracy of 94.92%, 99.67%, and 90.75% was obtained on ESC-50, ESC-10 and UrbanSound8K datasest, respectively. In order to simulate the classification performance of the framework in the actual environment, the framework was also evaluated on a self-made sound dataset with different signal-to-noise ratios. The experimental results show that the proposed audio classification framework has good robustness and feasibility.