In recent years, there has been a notable increase in research on machine learning models for music retrieval and generation systems that are capable of taking natural language sentences as inputs. However, there is a scarcity of large-scale publicly available datasets, consisting of music data and their corresponding natural language descriptions known as music captions. In particular, non-musical information such as suitable situations for listening to a track and the emotions elicited upon listening is crucial for describing music. This type of information is underrepresented in existing music caption datasets due to the challenges associated with extracting it directly from music data. To address this issue, we propose a method for generating music caption data that incorporates non-musical aspects inferred from music thumbnail images, and validated the effectiveness of our approach through human evaluations. Additionally, we created a dataset with approximately 360,000 captions containing non-musical aspects. Leveraging this dataset, we trained a music retrieval model and demonstrated its effectiveness in music retrieval tasks through evaluation.

本研究针对现有音乐描述数据集中缺乏非音乐信息的问题，提出了一种从音乐缩略图中提取音乐印象的方法。研究通过人类评估验证了这种新方法的有效性，并构建了一个包含约36万个非音乐方面描述的数据集，以训练音乐检索模型并展示其在检索任务中的有效性。

音乐的印象能否从缩略图中提取？