1 research outputs found
CITY CLASSIFICATION FROM MULTIPLE REAL-WORLD SOUND SCENES
The majority of sound scene analysis work focuses on one of two clearly
defined tasks: acoustic scene classification or sound event detection. Whilst
this separation of tasks is useful for problem definition, they inherently
ignore some subtleties of the real-world, in particular how humans vary in how
they describe a scene. Some will describe the weather and features within it,
others will use a holistic descriptor like `park', and others still will use
unique identifiers such as cities or names. In this paper, we undertake the
task of automatic city classification to ask whether we can recognize a city
from a set of sound scenes? In this problem each city has recordings from
multiple scenes. We test a series of methods for this novel task and show that
a simple convolutional neural network (CNN) can achieve accuracy of 50%. This
is less than the acoustic scene classification task baseline in the DCASE 2018
ASC challenge on the same data. A simple adaptation to the class labels of
pairing city labels with grouped scenes, accuracy increases to 52%, closer to
the simpler scene classification task. Finally we also formulate the problem in
a multi-task learning framework and achieve an accuracy of 56%, outperforming
the aforementioned approaches.Comment: Accepted to WASPAA 201