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This study introduces a novel training paradigm, audio difference learning,for improving audio captioning. The fundamental concept of the proposedlearning method is to create a feature representation space that preserves therelationship between audio, enabling the generation of captions that detailintricate audio information. This method employs a reference audio along withthe input audio, both of which are transformed into feature representations viaa shared encoder. Captions are then generated from these differential featuresto describe their differences. Furthermore, a unique technique is proposed thatinvolves mixing the input audio with additional audio, and using the additionalaudio as a reference. This results in the difference between the mixed audioand the reference audio reverting back to the original input audio. This allowsthe original input's caption to be used as the caption for their difference,eliminating the need for additional annotations for the differences. In theexperiments using the Clotho and ESC50 datasets, the proposed methoddemonstrated an improvement in the SPIDEr score by 7% compared to conventionalmethods.

Audio Difference Learning for Audio Captioning