Asymmetric Kernel Convolutional Neural Network for Acoustic Scenes Classification

In this work, we propose an Asymmetric Kernel Convolutional Neural Network (AKCNN) for Acoustic Scenes Classification (ASC). Its kernel shape is not the traditional square but asymmetric in width and height. It also uses Weight Normalization (WN) to accelerate the training process because it can early converge the training loss and accuracy. The best of all, WN can help increase the accuracy of ASC. TUT Acoustic Scenes 2016 Dataset [1] is used for evaluation. The result shows that AKCNN achieves accuracy 86.7%. If we rank the score in DCASE2016 ASC Challenge, it shows that the system would have a higher score than the 5th place.