MCMA:一个象征性的多音轨反双关音乐档案

IF 0.6 0 MUSIC Empirical Musicology Review Pub Date : 2021-12-10 DOI:10.18061/emr.v16i1.7637
Anna Aljanaki, Stefano Kalonaris, G. Micchi, Eric Nichols
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引用次数: 2

摘要

我们推出多音轨反曲音乐档案(MCMA),可在https://mcma.readthedocs.io),一个专门策划的作品符号数据集,为任何给定的复调作品组成独立的声音。到目前为止,MCMA只由巴洛克曲目组成,但我们的目标是将其扩展到其他对位音乐。MCMA符合FAIR,它适用于音乐学任务,如(计算)分析或教育,因为它通过明确和独立的表示来突出对位互动。此外,它提供了对自然语言处理领域的最新进展(例如,神经机器翻译)的更恰当使用。例如,MCMA在用于音乐生成的基于语言的机器学习模型的上下文中可能特别有用。尽管目前规模不大,但我们相信MCMA是在线对位音乐数据库的重要补充,因此我们向更广泛的社区开放,希望MCMA能够在我们的努力之外继续发展。在这篇文章中,我们提供了这个语料库的基本原理,提出了可能的用例,概述了汇编过程(数据来源和处理),并在撰写本文时对语料库进行了简要的统计分析。最后,讨论了我们今后努力开展的工作。
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MCMA: A Symbolic Multitrack Contrapuntal Music Archive
We present Multitrack Contrapuntal Music Archive (MCMA, available at https://mcma.readthedocs.io), a symbolic dataset of pieces specifically curated to comprise, for any given polyphonic work, independent voices. So far, MCMA consists only of pieces from the Baroque repertoire but we aim to extend it to other contrapuntal music. MCMA is FAIR-compliant and it is geared towards musicological tasks such as (computational) analysis or education, as it brings to the fore contrapuntal interactions by explicit and independent representation. Furthermore, it affords for a more apt usage of recent advances in the field of natural language processing (e.g., neural machine translation). For example, MCMA can be particularly useful in the context of language-based machine learning models for music generation. Despite its current modest size, we believe MCMA to be an important addition to online contrapuntal music databases, and we thus open it to contributions from the wider community, in the hope that MCMA can continue to grow beyond our efforts. In this article, we provide the rationale for this corpus, suggest possible use cases, offer an overview of the compiling process (data sourcing and processing), and present a brief statistical analysis of the corpus at the time of writing. Finally, future work that we endeavor to undertake is discussed.
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