Performance Analysis of Convolutional Neural Networks with Different Optimizers and Hybrid Pooling Methods for Sound Classification

Convolutional Neural Networks (CNNs) are used for the sound signal classification successfully due to their capabilities of deep architecture with pooling schemes. Since the pooling methods in CNN work as regularization to minimize the effect of overfitting which further improves the performance of CNN. Various pooling techniques are proposed to enhance the performance of CNN for classification, but mostly all pooling approaches are considered for the classification of images. Sound samples are of a different nature from the static images and the temporal sequences of sounds play a crucial role in the digitalization of sound samples. The presented work of this paper considered the different pooling schemes with convolutional neural networks to improve the classification accuracy for environment sounds. In this approach, two different CNN architectures are implemented and analyzed with various pooling schemes at local and global levels. Further, the paper presented a new pooling scheme at the local and global pooling aspects to improve the performance. This new pooling scheme is considered as max-min difference pooling. The simulated results are obtained on the UrbanSound8k dataset with different optimizers and proposed pooling schemes. The experimental results indicate improved performance of CNN model with max-min difference pooling with global aspect