Music, minds, computers: Analytical and creative perspectives

Course Information
TitleΜουσική, Νους, Υπολογιστές: Αναλυτικές και δημιουργικές προσεγγίσεις / Music, minds, computers: Analytical and creative perspectives
CodeΤΠ2006
FacultyFine Arts
SchoolMusic Studies
Cycle / Level1st / Undergraduate
Teaching PeriodSpring
CommonNo
StatusActive
Course ID600013921

Programme of Study: PPS Tmīmatos Mousikṓn Spoudṓn (2017-sīmera)

Registered students: 7
OrientationAttendance TypeSemesterYearECTS
Musicology / Music EducationElective Courses846
Music CompositionElective Courses846

Class Information
Academic Year2018 – 2019
Class PeriodSpring
Faculty Instructors
Instructors from Other Categories
Weekly Hours3
Class ID
600124846
Course Type 2016-2020
  • Scientific Area
Course Type 2011-2015
Knowledge Deepening / Consolidation
Mode of Delivery
  • Face to face
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
Prerequisites
Required Courses
  • ΤΠ1001 Introduction to music technology / informatics
  • ΤΠ1002 Introduction to musical acoustics
Learning Outcomes
This course introduces contemporary theories and applications in the domain of computational musicology. This branch of musicology deals with the systematic analysis and description of music phenomena via the development of appropriate computer programs. The course aims to familiarise students with the use of computational methodology in the investigation of musical phenomena. A number of music topics (such as perception of rhythm, harmony, motivic structure) that are already known to students are presented from a different viewpoint: the target is the systematic description of a particular music task (primarily from a cognitive perspective) such that a computational model may be developed and tested. By the end of the course students are expected to understand the methodology of musical computational modeling and to be able to apply it on a selected music topic by means of the systematic description of the particular problem, the application of an existing computational model on new musical data and the evaluation of the resulting output.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Work autonomously
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Appreciate diversity and multiculturality
  • Be critical and self-critical
Course Content (Syllabus)
Music, Mind, and Computers (Introduction). Score extraction from audio. Voice/Stream segregation. Musical surface segmentation. Rhythm induction. Musical similarity and motivic analysis. Determination of harmonic structure. Temporal and hierarchic organisation. Expressive score performance. Specialised lectures depending on the topic of the main project(s) in a specific semester. Individual and/or group meetings with students regarding the project.
Educational Material Types
  • Notes
  • Slide presentations
  • Multimedia
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
Description
Powerpoint presentations, Interactive teaching presentation the use of computational models in music, elearning course material
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures782.6
Reading Assigment170.6
Interactive Teaching in Information Center130.4
Project391.3
Exams30.1
Total1505
Student Assessment
Description
Students are asked to work on a project that investigates one particular music problem; they are asked to present the appropriate theoretical background from a musicological, cognitive and computational perspective, apply a given computational model on their own music data, evaluate the outcome of the computational system, suggest potential improvements, and, finally, provide a critical evaluation of the overall methodology. Student assessment and grading is based on the final submitted project. Overall participation and collaboration with student is taken into account along with the oral presentation/examination of the project at the end of the course.
Student Assessment methods
  • Written Assignment (Summative)
  • Oral Exams (Formative)
Bibliography
Course Bibliography (Eudoxus)
Δίνονται σημειώσεις και εξειδικευμένα άρθρα/βιβλία ανάλογα με την εργασία.
Additional bibliography for study
Καμπουρόπουλος, Α. (2013) Εισαγωγή στην Υπολογιστική Μουσικολογία. Στον συλλογικο τομο Εισαγωγη στη Μουσικολογια και στις Μουσικες Επιστημες. Ε. Νικα-Σαμψων (Επ.), University Studio Press, Thessaloniki. Cambouropoulos, E. (1998) Towards a General Computational Theory of Musical Structure. PhD Thesis, Faculty of Music and Department of Artificial Intelligence, University of Edinburgh. Διατίθεται στην ιστοσελίδα: http://users.auth.gr/~emilios Dixon, S. (2001). Automatic extraction of tempo and beat from expressive performances. Journal of New Music Research, 30 (1), 39–58. Handel, S. (1989) Listening. An Introduction to the Perception of Auditory Events. The MIT Press, Cambridge (Ma). Huron, D. (2001) Tone and Voice: A Derivation of the Rules of Voice-Leading from Perceptual Principles. Music Perception, 19(1):1-64. Kωσταρίδου-Ευκλείδη, Α. (1992). Γνωστική Ψυχολογία. Θεσσαλονίκη: Art of Text. Leman, M. (ed.) (1997) Music, Gestalt and Computing. Springer-Verlag, Berlin. Lerdahl, F. and Jackendoff, R. (1983) A Generative Theory of Tonal Music. The MIT Press, Cambridge (Ma). Temperley, D. (2001) The Cognition of Basic Musical Structures. The MIT Press, Cambridge (Ma). Tzanetakis, G., Kapur, A., Schloss, W. A., & M. Wright, (2007) "Computational Ethnomusicology", Journal of Interdisciplinary Music Studies. 1(2), pp. 1-24, 2007. Widmer, G. and Goebl, W. (2004). Computational Models of Expressive Music Performance: The State of the Art. Journal of New Music Research 33(3), 203-216.
Last Update
01-06-2021