When the existing spectrogram separation methods are used for acoustic scene classification research

the classification accuracy of these methods is not high. To solve the problem

an acoustic scene classification method based on Mel-spectrogram separation and long-distance self-calibration convolutional neural network (LSCNet) was proposed. Firstly

the working principles of spectrogram harmonic/percussive-source separation were presented. A Mel-spectrogram separation algorithm was proposed

which can separate the Mel-spectrogram into harmonic components

percussive source components

and residual components. Then

LSCNet was designed combining self-calibration convolutional network and residual enhancement mechanism. The model adopts frequency domain self-correction algorithm and long-distance enhancement mechanism to retain the original information of the feature map

strengthens the correlation between deep and shallow features through residual enhancement mechanism and channel attention enhancement mechanism

and combines multi-scale feature fusion module to further extract the effective information of the output layer in model training. Finally

acoustic scene classification experiments were conducted on Urbansound8K and ESC-50 datasets. Experimental results show that the Mel-spectrogram residual components (MSRC) could specifically reduce the influence of background noise

thereby indicating a better classification performance. The LSCNet could realize the attention to the frequency domain information in the feature map

and its best classification accuracy reached 90.1% and 88% respectively

which verified the effectiveness of the proposed method.