Computational Analysis of Yaredawi YeZema Silt in Ethiopian Orthodox Tewahedo Church Chants

2024-12-25

AI Summary

This study addresses the longstanding absence of systematic musicological and computational analysis of the “Yaredawi YeZema Silt” modal system—rooted in the tradition of Saint Yared—in Ethiopian Orthodox chant. We introduce the first publicly available, expertly annotated audio dataset of this repertoire and propose a computationally tractable modeling framework centered on the Stabilized Pitch Contour Distribution (SPCD) as a robust melodic feature. This approach overcomes the core challenge of quantifying orally transmitted sacred music, enabling automatic modal classification of ancient chant traditions. A lightweight neural network classifier built upon SPCD achieves significantly higher accuracy than conventional hand-crafted feature methods on the new dataset, while its outputs align closely with ethnomusicological theory—establishing cross-disciplinary validation. The work advances both digital preservation of endangered liturgical chant and the methodological foundations of computational ethnomusicology.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

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.

Problem

• Ethiopian Orthodox Chant
• Saint Yared's Rule
• Cultural Value

Innovation

• Modern Music Technology
• Statistical Pitch Analysis
• Ethiopian Orthodox Chant Database