Title: Learning from Between-class Examples for Deep Sound Recognition,
Abstract: Deep learning methods have achieved high performance in sound recognition
tasks. Deciding how to feed the training data is important for further
performance improvement. We propose a novel learning method for deep sound
recognition: Between-Class learning (BC learning). Our strategy is to learn a
discriminative feature space by recognizing the between-class sounds as
between-class sounds. We generate between-class sounds by mixing two sounds
belonging to different classes with a random ratio. We then input the mixed
sound to the model and train the model to output the mixing ratio. The
advantages of BC learning are not limited only to the increase in variation of
the training data; BC learning leads to an enlargement of Fisher's criterion in
the feature space and a regularization of the positional relationship among the
feature distributions of the classes. The experimental results show that BC
learning improves the performance on various sound recognition networks,
datasets, and data augmentation schemes, in which BC learning proves to be
always beneficial. Furthermore, we construct a new deep sound recognition
network (EnvNet-v2) and train it with BC learning. As a result, we achieved a
performance surpasses the human level.