PDMX：用於符號音樂處理的大規模公共領域MusicXML數據集

PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing

September 17, 2024

作者: Phillip Long, Zachary Novack, Taylor Berg-Kirkpatrick, Julian McAuley

cs.AI

摘要

近期生成式 AI-音樂系統的蓬勃發展引起了許多關於數據版權、從音樂家授權音樂以及開源 AI 與大型知名公司之間衝突的問題。這些議題凸顯了對公開可用、無版權的音樂數據的需求，特別是對於象徵音樂數據的需求嚴重不足。為了緩解這個問題，我們提出了 PDMX：一個大規模的開源數據集，包含超過 25 萬個從樂譜分享論壇 MuseScore 收集的公有領域 MusicXML 樂譜，據我們所知，這是目前最大的可用的無版權象徵音樂數據集。PDMX 還包括豐富的標籤和用戶互動元數據，使我們能夠高效地分析數據集並篩選出高質量的用戶生成樂譜。通過我們的數據收集過程提供的額外元數據，我們進行了多軌音樂生成實驗，評估 PDMX 的不同代表性子集如何導致下游模型中的不同行為，以及如何使用用戶評分統計作為數據質量的有效衡量標準。示例可在 https://pnlong.github.io/PDMX.demo/ 找到。

English

The recent explosion of generative AI-Music systems has raised numerous concerns over data copyright, licensing music from musicians, and the conflict between open-source AI and large prestige companies. Such issues highlight the need for publicly available, copyright-free musical data, in which there is a large shortage, particularly for symbolic music data. To alleviate this issue, we present PDMX: a large-scale open-source dataset of over 250K public domain MusicXML scores collected from the score-sharing forum MuseScore, making it the largest available copyright-free symbolic music dataset to our knowledge. PDMX additionally includes a wealth of both tag and user interaction metadata, allowing us to efficiently analyze the dataset and filter for high quality user-generated scores. Given the additional metadata afforded by our data collection process, we conduct multitrack music generation experiments evaluating how different representative subsets of PDMX lead to different behaviors in downstream models, and how user-rating statistics can be used as an effective measure of data quality. Examples can be found at https://pnlong.github.io/PDMX.demo/.