Acoustic Scene Classification Using Deep Convolutional Neural Network via Transfer Learning

Abstract

We use deep convolutional neural network via transfer learning for Acoustic Scene Classification (ASC). For this purpose, a powerful and popular deep learning architecture — Residual Neural Network (Resnet) is adopted. Transfer learning is used to fine-tune the pre-trained Resnet model on the TUT Urban Acoustic Scenes 2018 dataset. Furthermore, the focal loss is used to improve overall performance. In order to reduce the chance of overfitting, data augmentation technique is applied based on mixup. Our best system has achieved an improvement of more than 10% in terms of class-wise accuracy with respect to the Detection and classification of acoustic scenes and events (DCASE) 2018 baseline system on the TUT Urban Acoustic Scenes 2018 dataset.