ETHIOPIAN ORTHODOX TEWAHIDO CHURCH AQUAQUAM  ZEMA CLASSIFICATION MODEL USING DEEP LEARNING

BAYE, TADESSE DAGNEW

dc.contributor.author	BAYE, TADESSE DAGNEW
dc.date.accessioned	2023-06-19T07:34:22Z
dc.date.available	2023-06-19T07:34:22Z
dc.date.issued	2023-03-16
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/15400
dc.description.abstract	Research has been done to automatically classify music genres based on their sound for music information retrieval (MIR) systems, music data analysis, and music transcription purposes. When the amount of music data increases, indexing and retrieving it becomes more challenging. Previous studies in this area had concentrated on the song classification, identification, prediction, and distinction of their music genre for modern music services. Aquaquam zema classification is one category of music information retrieval. One of the traditional forms of education in Ethiopian Orthodox Tewahdo Church (EOTC) is aquaquam zema, in which the priests perform with a measured sound while dressed secular clothes. It is closely related to music and rhythm because it is a secular art. The knowledge gap between modern and traditional education on the zema genre is primarily what pushes us in this approach because the majority of students in this traditional school do not have a complete understanding of the zema genre. We create a model to categorize the sound signal of aquaquam zema into their genre to help with this constraint. Five major zema kinds can be used to categorize aquaquam zema Zimame (ዝማሜ), Qum (Tsinatsel)(ቁም), Meregd (መረግድ), Tsifat (ጽፋት) and Amelales (አመላለስ). We obtained the audio data from the Aquaquam bet and recorded it using smartphones and the website to get this classification. After data collection, we begin to preprocess audio, segment audio with predetermined lengths, convert audio to visual representations, and produce spectrogram images. Then we create a model by extracting features and classifying them using a deep learning approach. We created a full-featured CNN model using the Softmax classifier. We achieve 97.5% of training accuracy and 91.76% for test accuracy by using the proposed model. Keywords: Aquaquam Zema, Deep Learning, spectrogram, Feature Extraction and Classification	en_US
dc.language.iso	en_US	en_US
dc.subject	Computing	en_US
dc.title	ETHIOPIAN ORTHODOX TEWAHIDO CHURCH AQUAQUAM ZEMA CLASSIFICATION MODEL USING DEEP LEARNING	en_US
dc.type	Thesis	en_US