Acoustic Scene Classification with Mismatched Recording Devices Using Mixture of Experts Layer
Truc Thi Kim Nguyen, Franz Pernkopf · 2019
Recently, a mismatch in acoustic conditions such as a temporal recording gap as well as different recording devices for the development and the evaluation data has been considered in Acoustic Scene Classification (ASC). This brings ASC closer to real-world conditions. In this paper, we address ASC with mismatching recording devices. This has been introduced as task 1B of the DCASE 2018 challenge. We proposed a flexible and robust model that uses a mixture of experts (MoE) layer replacing the fully connected dense layer such that each expert can adapt to the specific domains of the data. Furthermore, we observe different Convolutional Neural Network (CNN) models as well as the number of the experts of the MoE dense layer using log mel features. In addition, we perform mixup data augmentation to enhance the robustness of our models. In experiments, the classification performance is 66.1% using 15 experts in the MoE dense layer with approximately 2M parameters. This outperforms the best model of task 1B of the DCASE 2018 challenge by 2.5% (absolute). This model uses an ensemble selection of 12 individual models with ~12M parameters.