The Internet Archive Music Dataset

2026-09-15

AI Summary

本文介绍了互联网档案音乐数据集(IAMD)，通过自动标注管道和音频分类模型解决大规模音乐片段标注问题，提供了一个包含超过34,000小时音频的公开音乐-标题数据集。

Abstract

We introduce the Internet Archive Music Dataset (IAMD), a large-scale collection of captioned music segments derived from the Internet Archive. To the best of our knowledge, IAMD constitutes the largest publicly available music-caption dataset to date with over 34,000 hours of audio, providing a valuable benchmark for training and evaluating music understanding and generative models. The dataset is built from content declared to be distributed under Creative Commons licenses, and cross-referencing with MusicBrainz is done to improve license information reliability. To annotate IAMD, we present an automatic captioning pipeline that augments base captions produced by an audio-language model (ALM) with textual metadata sourced from the Internet Archive and imputed metadata obtained using audio classification models. Caption quality is assessed objectively and subjectively, and results indicate that the annotation pipeline is reliable and does not degrade caption quality with scaling.

Problem

• music dataset
• captioned music segments
• benchmark
• music understanding
• generative models

Innovation

• Internet Archive Music Dataset
• Automatic Captioning Pipeline
• Audio-Language Model