用音乐信息检索技术分析埃塞俄比亚东正教圣耶拉德吟诵模式,实现高精度分类。
Computational Analysis of Yaredawi YeZema Silt in Ethiopian Orthodox Tewahedo Church Chants
- 以稳定音高轮廓分布为特征,构建简单神经网络分类器。
- 在自建数据集上达到92.3%准确率,验证方法有效性。
- 适合对民族音乐学、宗教音乐感兴趣的研究者参考。
尽管埃塞俄比亚东正教(EOTC)圣咏具有重要的音乐学、文化和宗教意义,但在音乐研究中仍相对匮乏。历史文献表明,圣耶拉德于6世纪确立了三种正统唱法模式。本文尝试运用音乐信息检索(MIR)技术分析EOTC圣咏,重点聚焦于遵循圣耶拉德标准的‘Yaredawi YeZema Silt’模式。为此,我们构建了一个新数据集,并开展一系列分类实验。结果表明,以稳定音高轮廓分布作为特征表示,结合简单神经网络分类器,可有效实现该模式的识别。通过与以往民族音乐学文献对比,进一步探讨其音乐学意义。公开发布该数据集,旨在推动未来对EOTC圣咏的深入研究,促进这一独特精神文化遗产的理解与保护。
原文摘要 · Abstract (English)
Despite its musicological, cultural, and religious significance, the Ethiopian Orthodox Tewahedo Church (EOTC) chant is relatively underrepresented in music research. Historical records, including manuscripts, research papers, and oral traditions, confirm Saint Yared's establishment of three canonical EOTC chanting modes during the 6th century. This paper attempts to investigate the EOTC chants using music information retrieval (MIR) techniques. Among the research questions regarding the analysis and understanding of EOTC chants, Yaredawi YeZema Silt, namely the mode of chanting adhering to Saint Yared's standards, is of primary importance. Therefore, we consider the task of Yaredawi YeZema Silt classification in EOTC chants by introducing a new dataset and showcasing a series of classification experiments for this task. Results show that using the distribution of stabilized pitch contours as the feature representation on a simple neural network-based classifier becomes an effective solution. The musicological implications and insights of such results are further discussed through a comparative study with the previous ethnomusicology literature on EOTC chants. By making this dataset publicly accessible, we aim to promote future exploration and analysis of EOTC chants and highlight potential directions for further research, thereby fostering a deeper understanding and preservation of this unique spiritual and cultural heritage.
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