  In this paper, we present an acoustic scene classification framework based on
a large-margin factorized convolutional neural network (CNN). We adopt the
factorized CNN to learn the patterns in the time-frequency domain by
factorizing the 2D kernel into two separate 1D kernels. The factorized kernel
leads to learn the main component of two patterns: the long-term ambient and
short-term event sounds which are the key patterns of the audio scene
classification. In training our model, we consider the loss function based on
the triplet sampling such that the same audio scene samples from different
environments are minimized, and simultaneously the different audio scene
samples are maximized. With this loss function, the samples from the same audio
scene are clustered independently of the environment, and thus we can get the
classifier with better generalization ability in an unseen environment. We
evaluated our audio scene classification framework using the dataset of the
DCASE challenge 2019 task1A. Experimental results show that the proposed
algorithm improves the performance of the baseline network and reduces the
number of parameters to one third. Furthermore, the performance gain is higher
on unseen data, and it shows that the proposed algorithm has better
generalization ability.
