Religious music tradition in Erzurum
2021
0 views
0 downloads
Advisor: Doç. Dr. Erhan Özden
Abstract (EN)
Erzurum has been one of the cities where Turkish and Islamic traditions have come to life for centuries and has been an influential city both for the region and for the Islamdom with its scientific, mystical, artistic and cultural accumulations that were formed and developed in this region. Home to many scholars, dervishes, minstrels and poets, this city is also very rich in terms of musical culture. Especially the mystical life in Erzurum has maintained its musical tradition from past to present. Erzurum's Religious music, which was composed in this way, has a local attitude and style with the forms performed both in mosques and lodges. It is extremely important that the taking under protection and transferring this music tradition, which emerged in the sufi custom in Erzurum and has survived to the present day by being transmitted orally, to the next generations. In this context, together with the poems of sufi divan poets from Erzurum which united with the folk music tunes and read in Erzurum dervish lodges; Verses, adhans, salas, prayers and hymns uttered by the beautiful voiced hafiz's, imam's and muezzin's in Erzurum mosques in a local style are discussed in this study.
Author
Dr. Abdusselam Akbaş
Institution
How to Cite
Abdusselam Akbaş (Master Thesis). Religious music tradition in Erzurum, 2021, İstanbul University.
Keywords
License
Tüm Hakları Saklıdır
This work is shared under the specified license terms.
More theses from İstanbul University
- In the covid 19 pandemic of female employees at a university hospital attitudes and affecting factors in nutrition of 9 months-6 years old children(2022)
- The perception of the right-wing movements in Turkey as to the 27 May Coup: 1960-1980(2020)
- Economic and social life in the Ottoman Empire according to the 1890 year's news of La Turquie Newspaper(2022)
- Land regime in the Umayyads period(2022)
- Merkel hücreli karsinomda tanısal ve prognostik belirteçler(2022)
- Use of machine learning methods in classification of respiratory system diseases(2021)