  We investigate an method for quantifying city characteristics based on
impressions of a sound environment. The quantification of the city
characteristics will be beneficial to government policy planning, tourism
projects, etc. In this study, we try to predict two soundscape impressions,
meaning pleasantness and eventfulness, using sound data collected by the
cloud-sensing method. The collected sounds comprise meta information of
recording location using Global Positioning System. Furthermore, the soundscape
impressions and sound-source features are separately assigned to the
cloud-sensing sounds by assessments defined using Swedish Soundscape-Quality
Protocol, assessing the quality of the acoustic environment. The prediction
models are built using deep neural networks with multi-layer perceptron for the
input of 10-second sound and the aerial photographs of its location. An
acoustic feature comprises equivalent noise level and outputs of octave-band
filters every second, and statistics of them in 10~s. An image feature is
extracted from an aerial photograph using ResNet-50 and autoencoder
architecture. We perform comparison experiments to demonstrate the benefit of
each feature. As a result of the comparison, aerial photographs and
sound-source features are efficient to predict impression information.
Additionally, even if the sound-source features are predicted using acoustic
and image features, the features also show fine results to predict the
soundscape impression close to the result of oracle sound-source features.
