Audio Difference Learning for Audio Captioning
Tatsuya Komatsu Yusuke Fujita Kazuya Takeda Tomoki Toda
This study introduces a novel training paradigm, audio difference learning,
for improving audio captioning. The fundamental concept of the proposed
learning method is to create a feature representation space that preserves the
relationship between audio, enabling the generation of captions that detail
intricate audio information. This method employs a reference audio along with
the input audio, both of which are transformed into feature representations via
a shared encoder. Captions are then generated from these differential features
to describe their differences. Furthermore, a unique technique is proposed that
involves mixing the input audio with additional audio, and using the additional
audio as a reference. This results in the difference between the mixed audio
and the reference audio reverting back to the original input audio. This allows
the original input's caption to be used as the caption for their difference,
eliminating the need for additional annotations for the differences. In the
experiments using the Clotho and ESC50 datasets, the proposed method
demonstrated an improvement in the SPIDEr score by 7% compared to conventional
methods.