Abstract:
Research has been conducted to automate the classification of music genres based on their sound,
with applications in music information retrieval (MIR) systems, music data analysis, and music
transcription. As the volume of music data continues to grow, indexing and retrieval pose
significant challenges. Previous studies have focused on classifying, identifying, predicting, and
distinguishing music genres for contemporary music services. However, the absence of a
standardized classification system for Zema genres hinders the successful preservation,
comprehension, and dissemination of this rich musical tradition. This study specifically addresses
the classification of Ethiopian Orthodox Tewahedo Church Zema, which falls within the domain
of music information retrieval. Geez Zema, a traditional form of education in the Ethiopian
Orthodox Tewahedo Church (EOTC), involves priests performing measured sounds while dressed
in secular attire. Due to its inherent connection to music and rhythm, it can be considered a form
of secular art. The primary motivation for this research stems from the knowledge gap between
modern and traditional education regarding the Zema genre, as many students in this traditional
school (Abinet School) lack a comprehensive understanding of it. Therefore, a model was
developed to categorize the sound of Geez Zema into its respective genre, aiming to address this
limitation.
Three major Zema types, namely Geez (ግዕዝ), Ezil (ዕዝል), and Araray (ዓራራይ), were utilized to
classify Geez Zema. Audio data was collected from the traditional religious school (Abinet School)
and recorded using smartphones and the different school's websites for teaching purposes.
Following data collection, the audio underwent preprocessing, including windowing with
predetermined durations of 10 seconds, 15 seconds, and 20 seconds. Then feature selection and
extraction were held and we selected nine different features. Subsequently, a model was created
by extracting features and employing a deep learning approach for classification. Specifically,
convolutional neural network (CNN), recurrent neural network (RNN), and convolutional
recurrent neural network (CRNN) models were developed using the softmax classifier. The CRNN
model with a window size of 20 seconds and a 90/10 train-test split achieved training accuracy of
98.9% and test accuracy of 97.89%, precision of 98 %, recall of 98%, and F1-score 98%.
