  Noise pollution significantly affects our daily life and urban development.
Urban Sound Tagging (UST) has attracted much attention recently, which aims to
analyze and monitor urban noise pollution. One weakness of the previous UST
studies is that the spatial and temporal context of sound signals, which
contains complementary information about when and where the audio data was
recorded, has not been investigated. To address this problem, in this paper, we
propose a multimodal UST system that deeply mines the audio and spatiotemporal
context together. In order to incorporate characteristics of different acoustic
features, two sets of four spectrograms are first extracted as the inputs of
residual neural networks. Then, the spatiotemporal context is encoded and
combined with acoustic features to explore the efficiency of multimodal
learning for discriminating sound signals. Moreover, a data filtering approach
is adopted in text processing to further improve the performance of
multi-modality. We evaluate the proposed method on the UST challenge (task 5)
of DCASE2020. Experimental results demonstrate the effectiveness of the
proposed method.
