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Environmental Sound Classification (ESC) is an important and challengingproblem, and feature representation is a critical and even decisive factor inESC. Feature representation ability directly affects the accuracy of soundclassification. Therefore, the ESC performance is heavily dependent on theeffectiveness of representative features extracted from the environmentalsounds. In this paper, we propose a subspectrogram segmentation based ESCclassification framework. In addition, we adopt the proposed ConvolutionalRecurrent Neural Network (CRNN) and score level fusion to jointly improve theclassification accuracy. Extensive truncation schemes are evaluated to find theoptimal number and the corresponding band ranges of sub-spectrograms. Based onthe numerical experiments, the proposed framework can achieve 81.9% ESCclassification accuracy on the public dataset ESC-50, which provides 9.1%accuracy improvement over traditional baseline schemes.

Sub-Spectrogram Segmentation for Environmental Sound Classification via Convolutional Recurrent Neural Network and Score Level Fusion