A CRNN with mixup and score-level fusion of four sub-spectrograms reaches 81.9% accuracy on ESC-50, a 9.1 percentage point gain over a plain CNN baseline.
Acoustic scene classification: Classifying environments from the sounds they produce,
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Sub-Spectrogram Segmentation for Environmental Sound Classification via Convolutional Recurrent Neural Network and Score Level Fusion
A CRNN with mixup and score-level fusion of four sub-spectrograms reaches 81.9% accuracy on ESC-50, a 9.1 percentage point gain over a plain CNN baseline.