Pregled bibliografske jedinice broj: 960635
Automated classification of Croatian traditional music
Automated classification of Croatian traditional music // Proceedings of 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, 2018. / Skala, Karolj (ur.).
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2018. str. 1028-1033 doi:10.23919/MIPRO.2018.8400188 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 960635 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Automated classification of Croatian traditional music
Autori
Strizrep, Ivan ; Sovic Krzic, Ana ; Sersic, Damir
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, 2018.
/ Skala, Karolj - Rijeka : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2018, 1028-1033
ISBN
978-953-233-097-7
Skup
41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2018)
Mjesto i datum
Opatija, Hrvatska, 21.05.2018. - 25.05.2018
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
classification, mel-frequency cepstral coefficients, machine learning, traditional music
Sažetak
Croatian traditional music is rich with different music styles. Four of them are on the UNESCO Representative list of the intangible cultural heritage of humanity: two-part singing and playing in the Istrian scale, Becarac singing and playing from Slavonia, Klapa multipart singing of Dalmatia and Ojkanje singing. Every region of Croatia is represented by different instruments, singing styles, rhythm and dynamics. This paper describes an automated classification of Croatian traditional music into regions. The regions are defined by historical and geographical factors and music style similarities: Slavonia, central Croatia, Međimurje, Istria&Kvarner and Dalmatia. Each region is presented with 20 typical music songs. A sample of each song lasts for 30 seconds. The primary used features are mel- frequency cepstral coefficients, as well as zero crossing rate and sound volume. Extracted features are used in machine learning. As a result, more than 80% of the songs are correctly classified. The result shows how specific Croatian traditional music is and how important is to preserve it for future generations.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Projekti:
HRZZ-IP-2014-09-2625 - Iznad Nyquistove granice (BeyondLimit) (Seršić, Damir, HRZZ ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
