  Acoustic scene classification (ASC) aims to identify the type of scene
(environment) in which a given audio signal is recorded. The log-mel feature
and convolutional neural network (CNN) have recently become the most popular
time-frequency (TF) feature representation and classifier in ASC. An audio
signal recorded in a scene may include various sounds overlapping in time and
frequency. The previous study suggests that separately considering the
long-duration sounds and short-duration sounds in CNN may improve ASC accuracy.
This study addresses the problem of the generalization ability of acoustic
scene classifiers. In practice, acoustic scene signals' characteristics may be
affected by various factors, such as the choice of recording devices and the
change of recording locations. When an established ASC system predicts scene
classes on audios recorded in unseen scenarios, its accuracy may drop
significantly. The long-duration sounds not only contain domain-independent
acoustic scene information, but also contain channel information determined by
the recording conditions, which is prone to over-fitting. For a more robust ASC
system, We propose a robust feature learning (RFL) framework to train the CNN.
The RFL framework down-weights CNN learning specifically on long-duration
sounds. The proposed method is to train an auxiliary classifier with only
long-duration sound information as input. The auxiliary classifier is trained
with an auxiliary loss function that assigns less learning weight to poorly
classified examples than the standard cross-entropy loss. The experimental
results show that the proposed RFL framework can obtain a more robust acoustic
scene classifier towards unseen devices and cities.
