LP-MusicCaps: LLM-Based Pseudo Music Captioning
Authors: name: SeungHeon Doh, name: Keunwoo Choi, name: Jongpil Lee, name: Juhan Nam

Automatic music captioning, which generates natural language descriptions for
given music tracks, holds significant potential for enhancing the understanding
and organization of large volumes of musical data. Despite its importance,
researchers face challenges due to the costly and time-consuming collection
process of existing music-language datasets, which are limited in size. To
address this data scarcity issue, we propose the use of large language models
(LLMs) to artificially generate the description sentences from large-scale tag
datasets. This results in approximately 2.2M captions paired with 0.5M audio
clips. We term it Large Language Model based Pseudo music caption dataset,
shortly, LP-MusicCaps. We conduct a systemic evaluation of the large-scale
music captioning dataset with various quantitative evaluation metrics used in
the field of natural language processing as well as human evaluation. In
addition, we trained a transformer-based music captioning model with the
dataset and evaluated it under zero-shot and transfer-learning settings. The
results demonstrate that our proposed approach outperforms the supervised
baseline model.