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