Analytical Musicology

In the field of analytical musicology, we have focused mainly on the question of the analysis of musical intervals, given the historical importance of this subject, especially in some ancient civilizations, and also in medieval Arabic texts and in more recent periods following the study of non-European music.


For the purposes of musicological analysis, we develop the Music22 package, written in Python. More specifically, our aim is to analyze modal music (melodically and rhythmically) from sound recordings, using the tools of MIR. For analyzes using symbolic data you can use the package Music21.

Lectures and participation to seminars

  • Oct. 15, 2018 : Données numériques et analyse des musiques traditionnelles.
    Séminaire LESC-CREM, Univ. Nanterre.
  • July 5, 2018 : Sound recording and archeology of Arab music: from Baron Erlanger to the era of Big Data.
    Assilah Forum 2018, Marroco.
  • June 19, 2018 : Vers un inventaire annoté des rythmes musicaux en Tunisie.
    ICTM, Essaouira.
  • June 29, 2017 : Analysis of modal recorded music : new approches, new methods.
    9th European Music Analysis Conference, Strasbourg.
  • March 21, 2017 : Toward an audio dataset of Musical Rhythms in Tunisia.
    Workshop "Cross-disciplinary and multicultural perspectives on musical rhythm and improvisation III", New York - Abu-Dhabi University.
  • Nov. 25, 2015 : Transmission and study of oral traditions in the age of the Internet and Big Data.
    Symposium "Know-how and Transmission of Music of Oral Tradition", Algeria/CNRPAH.
  • July 19, 2015 : Probability Density Functions and Correlation Coefficients as melodic analysis tools.
    Meeting on the maqām-s and ṭubūʿ-s of Arabic Music, Saint-Esprit of Kaslik University.
  • June 11, 2015 : A Simple Method for a Melodic Classification based on Pitch and Scale Analysis.
    5th International Workshop on Folk Music Analysis, Pierre et Marie-Curie University.
  • Nov. 28, 2014 : Introduction to the MIR (Music Information Retrieval): Domains and Applications.
    Seminar of the U2S Lab., École Nationale des Ingénieurs.
  • Nov. 10, 2014 : « Musique et émotion » : une introduction.
    Mini-symposium "ICT, music, emotion and creation" organized by A3T (Association Tunisienne des Techniques des Télécommunications), with the participation of the British Council, City of Sciences.
  • Dec. 20, 2010 : Computer-assisted music and intervals of Oriental Music.
    Musical Days of Carthage / Download files PureData, National Library of Tunisia.
  • Dec. 17, 2008 : Computer-aided modal analysis: state of research and prospects.
    SYmposium "musical analysis" organized by the CUNTIC, Higher Institute of Music.
  • March 12, 2008 : Musicological object and anthropological object: a study of complementarities in the determination of a musical identity.
    Symposium "music and anthropology", Institute of Arts and Crafts - Music and Musicology Department.

My beginnings toward a method for musical intervals analysis

Below is a set of Flash files that we have made in 2002-2004. This was our first attempt to analyze multimedia files. During these first experiments, we have used the Praat software to get the fundamental pitch of a melody (Pitch) from an audio file. The determination of a reference frequency (the frequency of the tonic or, most often, the most present note) was done manually, and the other frequencies were calculated according to the epimorous (n + 1) / n) ratios; The frequencies selected are the closest to the frequencies actually present, according to the melodic curve.


Gulati, Sankalp. 2016. Computational Approaches for Melodic Description in Indian Art Music Corpora. PhD, Universitat Pompeu Fabra, 2016.
Adams, N.H., M.A. Bartsch, and G.H. Wakefield. 2006. Note segmentation and quantization for music information retrieval. IEEE Transactions on Audio, Speech and Language Processing 14: 131–141. doi:10.1109/TSA.2005.854088.
Adams, Norman H. 2002. Automatic segmentation of sung melodies. Citeseer.
Ali C. Gedik, Barış Bozkurt, and Cem Çırak. 2012. A computational study on divergence between theory and practice of tanbur fretting. Journal of interdisciplinary music studies 6: 87–113.
Aoyagi, Takahiro. 2004. Sayr of Maqām Rāst: A performance rule of a melodic mode in arab music examined through a reconstruction task.
Bariş Bozkurt. 2012. Features for analysis of Makam music. In Serra X, Rao P, Murthy H, Bozkurt B, editors. Proceedings of the 2nd CompMusic Workshop; 2012 Jul 12-13; Istanbul, Turkey. Barcelona: Universitat Pompeu Fabra; 2012. p. 61-65.
Chen, C.-C.J., and R. Miikkulainen. 2001. Creating melodies with evolving recurrent neural networks. In International Joint Conference on Neural Networks, 2001. Proceedings. IJCNN ’01, 3:2241–2246 vol.3. doi:10.1109/IJCNN.2001.938515.
Conklin, Darrell. 2006. Melodic analysis with segment classes. Machine Learning 65: 349–360. doi:10.1007/s10994-006-8712-x.
Conklin, Darrell, and Ian H. Witten. 1995. Multiple viewpoint systems for music prediction. Journal of New Music Research 24: 51–73. doi:10.1080/09298219508570672.
Dannenberg, Roger B., William P. Birmingham, Bryan Pardo, Ning Hu, Colin Meek, and George Tzanetakis. 2007. A comparative evaluation of search techniques for query-by-humming using the MUSART testbed. Journal of the American Society for Information Science and Technology 58: 687–701. doi:10.1002/asi.20532.
D’Annunzio, David, and Kenneth Ning. 2012. Learning Classes of Melodies with a Recurrent Neural Network.
Efrat, Alon, Quanfu Fan, and Suresh Venkatasubramanian. 2007. Curve matching, time warping, and light fields: New algorithms for computing similarity between curves. Journal of Mathematical Imaging and Vision 27: 203–216.
Endo, Tsukasa, S.-I. Ito, Yasue Mitsukura, and Minoru Fukumi. 2008. The Music Analysis Method Based on Melody Analysis. In Control, Automation and Systems, 2008. ICCAS 2008. International Conference on, 2559–2562.
Eren Özek. 2012. 07-Eren-Ozek-2nd-CompMusic-Workshop-2012.pdf. In Proc. of the 2nd CompMusic Workshop (Istanbul, Turkey, July 12-13, 2012). Istanbul, Turkey.
Frieler, Klaus, Frank Höger, and Jörg Korries. 2014. Meloworks - an integrated retrieval, analysis and E-learning platform for melody research. Accessed February 15.
Frieler, Klaus, and Daniel Müllensiefen. 2005. The simile algorithm for melodic similarity. Proceedings of the Annual Music Information Retrieval Evaluation exchange.
Féki, Soufiane. 2006. Musicologie, Sémiologie ou Ethnomusicologie Quel cadre épistémologique, quelles méthodes pour l’analyse des musiques du maqâm ? Eléments de réponse à travers l’analyse de quatre taqsîms. Paris: Sorbonne - Paris 4.
Grachten, Maarten, Josep Ll Arcos, and Ramon Lopez de Mantaras. 2004. Melodic similarity: Looking for a good abstraction level.
Gómez, Emilia, and Perfecto Herrera. 2008. Comparative analysis of music recordings from western and non-western traditions by automatic tonal feature extraction.
Hähnel, H, and K Schellnack. 1975. [The Madura-foot -- differential diagnosis of the injured foot]. Beiträge zur Orthopädie und Traumatologie 22: 109.
Ioannidis, L, E. Gómez, and P. Herrera. 2011. Tonal-based retrieval of Arabic and Middle-East music by automatic makam description.
León, Pedro J. Ponce De, and José M. Iñesta. 2004. Statistical description models for melody analysis and characterization. In In Proceedings of the 2004 International Computer Music Conference, 149–156.
Li, Ming, and Ronan Sleep. 2004. Melody classification using a similarity metric based on kolmogorov complexity. Sound and Music Computing: 126–129.
Marsden, Alan. 2007. Timing in music and modal temporal logic. Journal of Mathematics and Music 1: 173–189. doi:10.1080/17459730701666887.
Marsden, Alan. 2012. Interrogating Melodic Similarity: A Definitive Phenomenon or the Product of Interpretation? Journal of New Music Research 41: 323–335. doi:10.1080/09298215.2012.740051.
Martínez, Emilio Molina. 2012. Automatic scoring of singing voice based on melodic similarity measures.
Meek, Colin, and William P. Birmingham. 2004. A comprehensive trainable error model for sung music queries. J. Artif. Intell. Res. (JAIR) 22: 57–91.
Melchior Fracas, and Paul Fournel. Les mélodies de Markov. Les mélodies de Markov.
Mongeau, Marcel, and David Sankoff. 1990. Comparison of musical sequences. Computers and the Humanities 24: 161–175. doi:10.1007/BF00117340.
Mullensiefen, D., and M. Pendzich. 2009. Court decisions on music plagiarism and the predictive value of similarity algorithms. Musicae Scientiae 13: 257–295. doi:10.1177/102986490901300111.
Müllensiefen, Daniel. 2009. Fantastic: Feature ANalysis Technology Accessing STatistics (In a Corpus): Technical Report v1. 5.
Müllensiefen, Daniel, and Klaus Frieler. 2004. Optimizing measures of melodic similarity for the exploration of a large folk song database. In Proceedings of the 5th International Conference on Music Information Retrieval. Barcelona: Universitat Pompeu Fabra, 274–280.
Müllensiefen, Daniel, and Klaus Frieler. 2006. Evaluating different approaches to measuring the similarity of melodies. In Data Science and Classification, 299–306. Springer.
Müllensiefen, Daniel, and Klaus Frieler. 2007. Modelling experts’ notions of melodic similarity. Musicae Scientiae 11: 183–210.
Müllensiefen, Daniel, and Geraint Wiggins. 2011. Polynomial functions as a representation of melodic phrase contour. In Systematic Musicology Empirical and Theoretical Studies, 63–88. Hamburger Jahrbuch Für Musikwissenschaft. Frankfurt am Main; New York: Peter Lang.
Naomi Cumming. 1992. Narmour’s Theory : Review by: Naomi Cumming 11.
Pearce, Marcus T. 2011. Time-series analysis of Music: Perceptual and Information Dynamics. Empirical Musicology Review 6.
Pearce, Marcus T., Daniel Müllensiefen, and Geraint A. Wiggins. 2010. Melodic grouping in music information retrieval: New methods and applications. In Advances in music information retrieval, 364–388. Springer.
Pearce, Marcus, and Geraint Wiggins. 2004. Improved Methods for Statistical Modelling of Monophonic Music. Journal of New Music Research 33: 367–385. doi:10.1080/0929821052000343840.
Sajjad Abdoli. 2011. Iranian traditional music Dastgah classification. In 2th International Society for Music Information Retrieval Conference (ISMIR 2011).
Salamon, Justin, and Emilia Gómez. 2012. Melody extraction from polyphonic music signals using pitch contour characteristics. Audio, Speech, and Language Processing, IEEE Transactions on 20: 1759–1770.
Sinanan, Samantha. 2010. The future is here: query by humming as an example of content-based music information retrieval. Library student journal 5.
Temperley, David, and Daphne Tan. 2013. Emotional connotations of diatonic modes. Music Perception: An Interdisciplinary Journal 30: 237–257.
Volk, A., and P. van Kranenburg. 2012. Melodic similarity among folk songs: An annotation study on similarity-based categorization in music. Musicae Scientiae 16: 317–339. doi:10.1177/1029864912448329.
Yoshihara, Y., and T. Miura. 2005. Melody Classification Using EM Algorithm. In , 1:204–210. IEEE. doi:10.1109/COMPSAC.2005.98.
Ünal, Erdem, Barış Bozkurt, and M. Kemal Karaosmanoğlu. 2012. N-gram based Statistical Makam Detection on Makam Music in Turkey using Symbolic Data. submitted to ISMIR.