Open-Set Acoustic Scene Classification with Deep Convolutional Autoencoders

Abstract

Acoustic scene classification is the task of determining the environment in which a given audio file has been recorded. If it is a priori not known whether all possible environments that may be encountered during test time are also known when training the system, the task is referred to as open-set classification. This paper contains a description of an open-set acoustic scene classification system submitted to task 1C of the Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2019. Our system consists of a combination of convolutional neural networks for closed-set identification and deep convolutional autoencoders for outlier detection. On the evaluation dataset of the challenge, our proposed system significantly outperforms the baseline system and improves the score from 0.476 to 0.621. Moreover, our submitted system ranked 3rd among all teams in task 1C.