  In this technical report, we present a joint effort of four groups, namely
GT, USTC, Tencent, and UKE, to tackle Task 1 - Acoustic Scene Classification
(ASC) in the DCASE 2020 Challenge. Task 1 comprises two different sub-tasks:
(i) Task 1a focuses on ASC of audio signals recorded with multiple (real and
simulated) devices into ten different fine-grained classes, and (ii) Task 1b
concerns with classification of data into three higher-level classes using
low-complexity solutions. For Task 1a, we propose a novel two-stage ASC system
leveraging upon ad-hoc score combination of two convolutional neural networks
(CNNs), classifying the acoustic input according to three classes, and then ten
classes, respectively. Four different CNN-based architectures are explored to
implement the two-stage classifiers, and several data augmentation techniques
are also investigated. For Task 1b, we leverage upon a quantization method to
reduce the complexity of two of our top-accuracy three-classes CNN-based
architectures. On Task 1a development data set, an ASC accuracy of 76.9\% is
attained using our best single classifier and data augmentation. An accuracy of
81.9\% is then attained by a final model fusion of our two-stage ASC
classifiers. On Task 1b development data set, we achieve an accuracy of 96.7\%
with a model size smaller than 500KB. Code is available:
https://github.com/MihawkHu/DCASE2020_task1.
