Computer Science Applied to Music
A.Y. 2020/2021
Learning objectives
The course objective is to introduce students to music applications of computer science, both considering written music and audio signals at different representation levels.
Expected learning outcomes
It is expected that students learn basic abilities for coding and processing of music information at various repesentation levels, considering both written music and audio signals.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
Second semester
The lessons will be held via the Zoom platform. Students can follow them both synchronously, based on the published timetable, and asynchronously, thanks to the recording made available to students on the Ariel platform.
If the numbers and regulations concerning social distancing allow it, it will be possible for students to attend the course even in presence.
Written examination for the theoretical part will be held in oral form during the emergency period.
If the numbers and regulations concerning social distancing allow it, it will be possible for students to attend the course even in presence.
Written examination for the theoretical part will be held in oral form during the emergency period.
Course syllabus
THEORETICAL PART.
Formal description of music information. Representation levels. Multilayer description and interaction. Special languages (DARMS, MUSIC V, SMDL, NIFF).
Audio information: linear codes; differential codes; lossless and lossy (MP3 & AAC) codes; audio & music features' recognition within audio signals.
Timbre programming: sampling, mathematical and physical models.
Performing information: the MIDI standard.
Symbolic information: optical music recognition (OMR); music archives; music information querying and retrieval by contents; music electronic publishing; analysis and pattern recognition of scores; generative models.
Structural information: processing and synthesis of symbolic information; Musical Petri Nets.
Multilayer information: MPEG4 SASL/SAOL, MPEG7, MPEG21, IEEE MX.
Technologies for Digital Rights Management.
EXPERIMENTAL PART.
Experimental activities in a specially equipped laboratory about:
- digital audio signal processing and feature extraction (MATLAB);
- digital score editing and processing (MuseScore);
- music information modelling (Petri nets);
- multi-layer representation of music information (IEEE 1599).
Formal description of music information. Representation levels. Multilayer description and interaction. Special languages (DARMS, MUSIC V, SMDL, NIFF).
Audio information: linear codes; differential codes; lossless and lossy (MP3 & AAC) codes; audio & music features' recognition within audio signals.
Timbre programming: sampling, mathematical and physical models.
Performing information: the MIDI standard.
Symbolic information: optical music recognition (OMR); music archives; music information querying and retrieval by contents; music electronic publishing; analysis and pattern recognition of scores; generative models.
Structural information: processing and synthesis of symbolic information; Musical Petri Nets.
Multilayer information: MPEG4 SASL/SAOL, MPEG7, MPEG21, IEEE MX.
Technologies for Digital Rights Management.
EXPERIMENTAL PART.
Experimental activities in a specially equipped laboratory about:
- digital audio signal processing and feature extraction (MATLAB);
- digital score editing and processing (MuseScore);
- music information modelling (Petri nets);
- multi-layer representation of music information (IEEE 1599).
Prerequisites for admission
Elements of audio digital signal processing, and computer programming.
Teaching methods
The theoretical part of the course is carried out through lectures, enriched by multimedia and web examples. The experimental part of the course alternates lectures and practical training for individuals or small groups.
Teaching Resources
Teaching materials are available at:
https://sntalampirasiam2p.ariel.ctu.unimi.it/v5/home/Default.aspx
Further materials can be downloaded from:
http://www.lim.di.unimi.it/teaching_courses_eng.php
https://sntalampirasiam2p.ariel.ctu.unimi.it/v5/home/Default.aspx
Further materials can be downloaded from:
http://www.lim.di.unimi.it/teaching_courses_eng.php
Assessment methods and Criteria
The examination consists in two tests: a written test, and an individual project test; both them are required.
The written test consists in about ten theoretical open questions .
The project test consists in the development of individual projects concerning methods, techniques, and software tools learned in the frame of experimental training activities of the course.
Each of the two tests gives an evaluation expressed in thirtieths. The average of the two gives the global evaluation obtained.
The written test consists in about ten theoretical open questions .
The project test consists in the development of individual projects concerning methods, techniques, and software tools learned in the frame of experimental training activities of the course.
Each of the two tests gives an evaluation expressed in thirtieths. The average of the two gives the global evaluation obtained.
INF/01 - INFORMATICS - University credits: 18
Lessons: 144 hours
Professors:
Haus Goffredo Maria, Ludovico Luca Andrea, Ntalampiras Stavros
Professor(s)
Reception:
Tuesday, 10.30 - 12.30 or by appointment
Laboratory of Music Informatics (LIM), Department of Computer Science, 4th floor