AUTOMATIC RESPIRATORY SOUND CLASSIFICATION VIA MULTI-BRANCH TEMPORAL CONVOLUTIONAL NETWORK

Automated classification of respiratory sounds has become an active research area in recent years. While recent studies have utilised deep learning methods to aid with respiratory sound classification, the performance is heavily influenced by the datasets available for respiratory sound classification tasks, which tend to be smaller and imbalanced. In this paper, we propose to explore the effectiveness of a multi-branch Temporal Convolutional Network (TCN) architecture integrated with Squeeze-and-Excitation Network (SEnet), a system denoted herein as MBTCNSE, for respiratory sound classification. To the best of the authors? knowledge, this is the first time that such a hybrid architecture has been employed for respiratory sounds classification. Experiments based on the ICBHI challenge respiratory sound dataset demonstrate the effectiveness of our method.