Ciamarone, LucianoBozkurt, BarisSerra, Xavier2021-05-192021-05-192019Ciamarone L, Bozkurt B, Serra X. Automatic Dastgah recognition using Markov Models. In: Kronland-Martinet R, Ystad S, Aramaki M, editors. CMMR 2019: Perception, Representations, Image, Sound, Music; 2019 Oct 14-18; Marseille, France. Cham: Springer; 2019. p. 157-65. (LNCS; no. 12631). DOI: 10.1007/978-3-030-70210-6_11http://hdl.handle.net/10230/47598Comunicació presentada al CMMR 2019: Perception, Representations, Image, Sound, Music, celebrat del 14 al 18 d'octubre de 2019 a Marsella, França.This work focuses on automatic Dastgah recognition of monophonic audio recordings of Iranian music using Markov Models. We present an automatic recognition system that models the sequence of intervals computed from quantized pitch data (estimated from audio) with Markov processes. Classification of an audio file is performed by finding the closest match between the Markov matrix of the file and the (template) matrices computed from the database for each Dastgah. Applying a leave-one-out evaluation strategy on a dataset comprised of 73 files, an accuracy of 0.986 has been observed for one of the four tested distance calculation methods.application/pdfeng© Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-70210-6_11info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1007/978-3-030-70210-6_11Mode recognitionDastgah recognitionIranian musicinfo:eu-repo/semantics/openAccess