Clotho: An Audio Captioning Dataset
Table 1: Translation metrics for the evaluation and testing splits. Bn, C, M, and R correspond to BLEUn, CIDEr, METEOR, and ROUGE, respectively.
['[BOLD] Metric', '[BOLD] B1', '[BOLD] B2', '[BOLD] B3', '[BOLD] B4', '[BOLD] C', '[BOLD] M', '[BOLD] R']
For example, the method outputs “water is running into a container into a”, while the closest reference caption is “water pouring into a container with water in it already”, or “birds are of chirping the chirping and various chirping” while the closest reference is “several different kinds of birds are chirping and singing”. The scores of the rest metrics reveal that the structure of the sentence and order of the words are not correct. These are issues that can be tackled by adopting either a pre-calculated or jointly learnt language model. In any case, the results show that the Clotho dataset can effectively be used for research on audio captioning, posing useful data in tackling the challenging task of audio content description.