  In this paper, we propose a framework for environmental sound classification
in a low-data context (less than 100 labeled examples per class). We show that
using pre-trained image classification models along with the usage of data
augmentation techniques results in higher performance over alternative
approaches. We applied this system to the task of Urban Sound Tagging, part of
the DCASE 2019. The objective was to label different sources of noise from raw
audio data. A modified form of MobileNetV2, a convolutional neural network
(CNN) model was trained to classify both coarse and fine tags jointly. The
proposed model uses log-scaled Mel-spectrogram as the representation format for
the audio data. Mixup, Random erasing, scaling, and shifting are used as data
augmentation techniques. A second model that uses scaled labels was built to
account for human errors in the annotations. The proposed model achieved the
first rank on the leaderboard with Micro-AUPRC values of 0.751 and 0.860 on
fine and coarse tags, respectively.
