Upon successful completion of the course, students will be able to:

• Understand in depth the nature of digital signals containing multimedia content.
• Know the basic data compression techniques.
• Evaluate the quality characteristics of multimedia content.
• Produce and process multimedia content.
• Develop interactive software applications using a scripting language.
• Create hypertext pages and embed multimedia content and interactive elements.

The course aims to enable students to acquire the following general competences:

• Search, analysis, and synthesis of data and information, using the necessary technologies.
• Adaptation to new situations.
• Autonomous work.
• Team work.
• Promotion of free, creative, and inductive thinking.

A general introduction to the technologies of encoding, processing, and representation of multimedia content. More specifically, the course content includes:

• Multimedia Content & Applications: Types of multimedia content and general applications.
• Digitization & Quality: General information digitization techniques and quality characteristics.
• Compression Principles: General principles of compression (lossy or lossless, perceptual or statistical encoding).
• Text: Encodings, representations and annotations (e.g., XML), word processing and spreadsheets, hypertext and hypermedia.
• Audio: Capture, synthesis, encoding, compression, processing, and related software.
• Image: Capture, synthesis, encoding, compression, processing, and related software.
• Video: Capture, synthesis, encoding, compression, processing, and related software.
• Animation: Capture, synthesis, encoding, compression, processing, and related software.
• Interactive Media: Interactive multimedia and applications based on scripting languages.
• Web Technologies: Web-based multimedia applications and internet services.

Z. Li, M. Drew, J. Liu, Fundamentals of Multimedia, Springer, 2014.

V. Costello, Multimedia Foundations, Focal Press, 2012.

The aim of the course is to introduce the design and development of computer programs based on the principles of structured programming. The course content is intended to familiarize students with the fundamental concepts of computer programming, provide an understanding of how computer programs are executed, and develop proficiency in the C programming language.

Upon successful completion of the course, students will be able to:

Develop programming skills and implement software using the C programming language.

Understand the fundamental principles of designing and implementing programs based on the structured programming paradigm.

Solve computational problems using computers.

Successfully undertake advanced courses in the curriculum that require computer programming knowledge and skills.

The course aims to develop the following general competencies:

Ability to search for, analyze, and synthesize data and information using appropriate technologies.

Ability to adapt to new situations.

Ability to work independently.

Ability to work effectively as a member of a team.

Ability to foster free, creative, and inductive thinking.

Computer operation. Computer architecture and memory organization. Data flow within a computer system. Instruction execution.

Software development. Software engineering. Software project life cycle. The phases of analysis, design, testing, and maintenance.

Software and programming languages. Source code and executable programs. The programming environment. Program compilation, debugging, and execution.

Structured programming. The importance of program structure. Fundamental principles and techniques of structured programming.

Algorithms: general concepts. Stepwise algorithms. Flowcharts. Pseudocode. Algorithmic problem solving. Searching and sorting algorithms.

The C programming language: characteristics and capabilities. Structure of C programs. Functions in C. Mathematical functions in C. Introductory concepts.

Data representation: characters, integers, and floating-point numbers. Basic data types, constants, variables, and the assignment operator. Number systems.

Input/output functions.

Operators: arithmetic, relational, and bitwise operators. Boolean expressions, relational expressions, logical expressions, and operator precedence. Compound operators. Pointers and memory addresses.

Program flow control structures. Nested control structures.

Iterative control structures (loops). Nested loops.

Functions in C: definition, declaration, and invocation. Returning values from functions. Function types. Passing addresses to functions. Storage classes. Automatic, external, and static variables. Variable scope and lifetime. Recursive functions.

One-dimensional arrays: declaration, initialization, input, and output. Array processing techniques. Character strings. String manipulation. Multidimensional arrays. Pointers and arrays. Arrays as function arguments.

Enumerations, structures, and unions.

File handling. File access functions.

Functions for dynamic memory management.

By completion of this course, students will be familiar with the theoretical background required for understanding basic sonic algorithmic processes and will have gained skills to develop their first computer music synthesis and sonic interaction algorithms in (mainly, but not exclusively) graphical audio programming environments.

The course aims at getting students acquainted with audio programming environments and providing them with the basic understanding and skills of audio programming. This can be considered as an introductory course in digital audio synthesis, as it covers rudimentary knowledge on digital sound and computer music. No prior computer programming skills are required.

More specifically, the course offers fundamental knowledge on the following subjects:
- computer music
- digital sound
- digital sound synthesis
- GUIs and audio interactive systems.

Theory:

• Introduction to audio programming environments and languages (graphical/modular versus textual)
• Bridging analog to digital sound
• Digital sound: sampling & quantization
• Τhe concepts of Oscillators, Unit Generators, Wavetables and Signal flowcharts
• Interpolation functions over time for sound control (amplitude envelopes, glissandi)
• Stereo imaging and panning
• Additive synthesis (example: Bell by Jean-Claude Risset)
• LFOs for tremolo and vibrato
• Real-time sound control and interaction (mouse and MIDI controllers)
• Overview and comparison of audio programming environments

Laboratory/hands-on:
The laboratory component demonstrates the implementation of sound synthesis and control techniques introduced in the lectures, using widely adopted music programming environments (MaxMsp/PureData/SuperCollider).

• Familiarization with the architecture of the selected programming environment.
• Creation of complex sounds using additive synthesis.
• Spatial positioning (panning) of the sound image within the stereo field.
• Implementation of tremolo and vibrato techniques.
• Design of a graphical user interface (GUI).
• Use of mapping functions for parameter control.

- Books:
[1] Online course material and hand-outs (‘E-class’ online platform)
[2] Διαμαντόπουλος Τ., Η μουσική των υπολογιστών
[3] Λώτης Θ., Διαμαντόπουλος Τ., Μουσική πληροφορική και μουσική με υπολογιστές
[4] Roads C., The Computer Music Tutorial
[5] Dodge C., Jerse T., Computer Music: Synthesis, Composition, and Performance
[6] Collins  N. & d’Escrivan J., The  Cambridge  Companion to  Electronic  Music
[7] Wilson S., Cottle D, Collins N., The Supercollider book

- Scientific journals:
[1] Computer Music Journal 
[2] Leonardo 

- Conferences: 
[1] International Computer Music Conference (ICMC) 
[2] Sound and Music Computing Conference (SMC) 
[3] International Conference on New Interfaces for Musical Expression (NIME)

The course is delivered through a series of independent laboratory exercises, providing students with hands-on experience in the operation of audio systems as well as in performing fundamental electroacoustic measurements.

Upon successful completion of the course, students will be able to:

• use a signal generator and an oscilloscope to perform laboratory measurements;
• understand the nature and characteristics of different types of audio signals and ensure their safe, reliable, and high-quality interconnection and transmission;
• understand the methods used for conducting fundamental electroacoustic measurements and perform these measurements in a laboratory environment;
• evaluate the quality and suitability of audio devices and systems based on their technical specifications.

The course aims to develop the following general competencies:

• the ability to search for, analyze, and synthesize data and information using appropriate technologies;
• the ability to adapt to new situations;
• the ability to work independently;
• the ability to work effectively as part of a team.

• Frequency response of audio devices and systems
• Microphone polar patterns
• Passive frequency filters
• Room equalization using a graphic equalizer
• Loudspeaker directivity pattern
• Measurement of crosstalk in multichannel audio devices
• Study of balanced audio signals and direct injection (DI)
• Sound reinforcement systems
• Measurement of acoustic source separation

Davis, Gary, and Gary D. Davis. The sound reinforcement handbook. Hal Leonard Corporation, 1989.

The aim of this course is to introduce the students to the theory, practice and applications of sound design. Sound design art applies in film, theatre, radio, interactive environments, web applications, multimedia and educational tools, and generally wherever there is the necessity to design what will be heard in a specific place and time.

The course aims to acquire the following general competencies: a) Search, analysis and synthesis of data and information, using the necessary technologies. b) Decision making. c) Autonomous work. d) Design and management of projects. e) Exercise of criticism & self-criticism f) Promotion of free, creative and inductive thinking g) Production of original artistic creation

This course aims at getting to get to know, familiarize and practice students with Sound Design. The thematic modules of the course include:

• Basic concepts and terminology in the arts of sound.
• Sound arts and the music of sounds.
• Areas of application of sound design.
• Basic techniques of transforming sounds by mechanical, analogue and digital means.
• Methodologies for collecting, classifying and categorizing audio material.
• Analysis of selected music and audiovisual artworks.
• Analysis of given script/scenario in sound categories.
• Sound dramaturgy.
• Functional categories of sound and music in their coexistence with action and moving pictures. (theatre, cinema, video, etc.)
• Creating stand-alone sound compositions (music with sounds).
• Individual assignment for the creation either of an original sound composition or sound design for moving image.

[1] Supervisor notes

[2] M. Chion, Audio-Vision: Sound on Screen, Columbia University Press, 1994 

[3] J.L. Drever, ‘Soundscape Composition: The Convergence of Ethnography and Acousmatic Music’, Organised Sound, 7(1), pp. 21-27, 2002. 

[4] EARS site. The ElectroAcoustic Resource Site (EARS), http://www.ears.dmu. ac.uk/. 

[5] S. Emmerson ‘The Relation of Language to Materials’ in Emmerson, S. (ed.), The Language of Electroacoustic Music, pp. 67-78, Basingstoke: Macmillan, 1986 

[6] L. Landy, Understanding the Art of Sound Organization, Cambridge, Mass.: MIT Press, 2007. 

[7] A. Licht, Sound Art- Beyond Music, Between Categories, New York: Rizzoli, 2007 

[8] D. Sonnenschein, Sound design: The expressive power of music, voice, and sound effects in cinema. Michael Wiese Productions, 2001

The aim of the course is to provide a solid foundation in digital signal processing and the development of audio applications, with an emphasis on applications that require real-time processing.

Upon successful completion of the course, students will be able to:

Understand the fundamental components involved in the development of audio applications.

Understand the programming practices required to access and configure audio hardware (e.g., number of channels, block size, sampling rate, etc.).

Be familiar with the programming tools and platforms used for the acquisition, analysis, synthesis, playback, and processing of digital audio signals.

Adapt digital signal processing methods to audio applications that require real-time processing.

Develop audio applications for desktop and mobile computing platforms.

The course aims to develop the following general competencies:

Ability to search for, analyze, and synthesize data and information using appropriate technologies.

Ability to adapt to new situations.

Ability to work independently.

Ability to work effectively as a member of a team.

Ability to promote free, creative, and inductive thinking.

Theory

Python

Variables

Lists, dictionaries, sets, and tuples

Functions and classes

Memory management

Audio files and digital audio encoding

Real-time audio

C++

Variables, functions, and classes

Pointers and vectors

Vector processing using functions

The JUCE framework

Programming audio effects in C++

Developing real-time audio applications with JUCE

Laboratory Exercises

Python

Variables and dictionaries

Programming static sound generators

Functions, classes, and sound generators

Memory management and audio encoding

Implementing simple effects for real-time audio processing

C++

Variables, functions, and classes

Vector operations using pointers

Implementing a distortion effect

Implementing sound generators with real-time parameter control

Implementing a delay effect

Implementing a tremolo effect

Implementing a chorus effect

The aim of the course is to provide the necessary knowledge for understanding the particularities involved in the management, storage, and processing of semantic information in music.

Upon successful completion of the course, students will be able to:

• Understand the use of such data and its application in systems for the recognition, classification, and retrieval of digital files
• Produce and process alternative forms of description of musical information based on standards such as MIDI or MusicXML
• Produce musical annotations by encoding temporally evolving musical information into a digital audio signal
• Explore and enrich large sets of musical data (music datasets, music corpora)

The course aims to help students acquire the following general competencies:

• Searching for, analyzing, and synthesizing data and information, using the necessary technologies
• Adapting to new situations
• Working autonomously
• Working in teams
• Working in an interdisciplinary environment
• Promoting free, creative, and inductive thinking

The subject of the course "Digital Representations of Music" concerns any type of descriptive information that can be associated with music. Such information includes, for example, metadata describing a musical work as a whole, as well as the description of its content in terms of notes, rhythmic values, and musical instruments.

The syllabus presents technologies related to issues such as:

• Music metadata
• Music notation file formats (score formats)
• Score rendering tools
• Software tools and programming libraries for the composition and processing of symbolic music notation (music APIs)

As indicative examples, the course presents standards such as: ID3, MIDI, MusicXML, MEI, Kern and Humdrum, Music21, and MusicBrainz. Emphasis is placed on the encoding of control commands for digital musical instruments and files using the MIDI protocol.

The practical exercises include reading, creating, and editing MIDI files with the Mido package (MIDI Objects for Python).

1. Ε. Νίκα-Σαμψών, Εισαγωγή στην μουσικολογία και τις μουσικές επισήμες, University Studio Press, 2019. [2] Χ. Αλεξανδράκη και Χ. Χουσίδης, Πρωτόκολλα Μουσικής Επικοινωνίας, Διδακτικές Σημειώσεις, Εγχειρίδιο Διδάσκοντος, 2011

Γενικά δεν υπάρχει ελληνικό συγγραμμα που να καλύπτει την ύλη του μαθήματος. Χρησιμοποιούνται οι σημειώσεις διδάσκοντος καθώς και διάφορες διαδικτυακές πηγές που αναφέρονται στη διδασκόμενη ύλη. 

Σημείώσεις διδάσκοντος. 

1. Αλεξανδράκη (2011), Πρωτόκολλα Μουσικής Επικοινωνίας
2. Αλεξανδράκη (2024), Δημιουργία, επεξεργασία κι απόδοση ψηφιακών αναπαραστάσεων της μουσικής με προγραμματιστικά εργαλεία της Python

The aim of the course is to introduce on the issues of the structure, operation, design and practice of electronic musical instruments and interactive musical systems used for music performance. Upon successful completion of the course, the student will be able to: - Analyse the structure and operation of electronic musical instruments. - To design original new musical instruments. - To experiment with the alternative methods of production & control of sound in music practice either in a group or individually.

The course aims to acquire the following general abilities: Search, analysis and synthesis of data and information, using the necessary technologies. Adapting to new situations. Autonomous work. Group work. Work in an interdisciplinary environment. Production of new research ideas. Promotion of free, creative and inductive thinking

This course aims at acquaintance and familiarization of students with topics related to the structure, operation, design and practice of electronic musical instruments. The thematic sections of the course include: 1) Electronic Musical Instruments: Types, Function, Historical References. 2) Early electronic musical instruments - Composers – Computer use 3) Musical instruments as interactive systems – Parts of the musical instrument. 4) Comparison of acoustic and electronic musical instruments. 5) Generalized model of a musical instrument. 6) Audio design of alternative electronic and hybrid musical instruments 7) Live electronics-History and practices 8) The design and evaluation of electronic musical instruments 9) Exercise of designing an electronic musical instrument for more than two musicians. 10) Selected presentations of the current developments of the field - Examples. - Research methodology, institutions, research organizations, relevant international conferences and journals. – Selection of sources. 11) Personal student work.

[1] Σημειώσεις Διδάσκοντος [2] P.R. Cook (ed.), Music, Cognition, and Computerized Sound – An Introduction to Psychoacoustics, The MIT Press, 1999. [3] Davis Hugh, Electronic Musical Instruments, New Grove Dictionary of Music, Macmillan Publishers Ltd, 1998-2002. [4] J. Eaton, “This is an Instrument" in Contemporary Music Review, Vol. 18 Part 3, 1999. [5] S. Emmerson, “Live' versus 'real-time”, Contemporary Music Review, 10(2), pp. 95-101, 1994. [6] J. Pressing, Jeff, “Cybernetic Issues in Interactive Performance Systems”, Computer Music Journal, Vol. 14 – 1, MIT Press, pp. 12-15, 1990. [7] C. Roads, The computer Music Tutorial, Massachusetts Institute of Technology, 1996. [8] L. Theremin, "Recollections", Contemporary Music Review, Vol. 18, Part 3, 1999.

The aim of the course is to introduce the Theory and Practices of Acoustic Ecology and their application to Sound Arts.

• Understand and use the specialized terminology of Acoustic Ecology in issues related to Sound.

• To learn and apply the research methodologies of the field (sound maps, categorization of sound sources and events, interpretation of the sounds of the environment as a means of acoustic communication etc) to the study and analysis of the sound environment.

• Create sound environments and/ or musical soundscape compositions.

The course aims to acquire the following general competencies: a) Search, analysis and synthesis of data and information, using the necessary technologies. b) Decision making. c) Autonomous work. d) Design and management of projects. e) Exercise of criticism & self-criticism f) Promotion of free, creative and inductive thinking g) Production of original artistic creation

• What is Acoustic Ecology? – Basic Terminology.
• Historical retrospection and evolution of Acoustic Ecology.
• Acoustic Communication, Soundscape research methodologies
• Acoustic Ecology in the sound arts. – Soundscape music.
• Soundwalking and field work practices- an introduction
• Soundscape Analysis I – indicative audio recordings, audio map and audio calendar.
• Soundscape Analysis II– methodology for recording data and drawing conclusions.
• Field exercise: Design of audio map and audio calendar.
• Field exercise: Audio recording
• Field Exercise: Soundwalk
• Composition of soundscape music.
• Listening and Analysing Sessions (3) selected relevant sound arts works 

 • Blesser, B. and Salter, L.R. (2007), Spaces Speak, Are You Listening?, Cambridge, Mass.: MIT Press.

• Kelman, A.Y. (2010), ‘Rethinking the Soundscape-A Critical Genealogy of a Key Term in Soundscape Studies ’, Senses & Society, 5(2), pp. 212-234, London: Berg.

• Krause, B. (2002), Wild Soundscapes – Discovering the Voice of the Natural World, Berkeley: Wilderness Press.

• Lopez, F. (2004), ‘Profound Listening and Environment Sound Matter’, In Cox, Ch., Warner, D., (eds.), Audio Culture: Readings in modern music, pp. 82–87, New York: Continuum.

• Oliveros, P. (2005), Deep Listening - A Composer’s Sound Practice, iUniverse, Inc, USA.

• Schafer, R.M. (2006), ‘The Music of the Environment’, in Cox, Ch. and Warner, D., (eds.), Audio Culture: Readings in modern music, pp. 29-39, New York: Continuum.

• Smalley, D. (2007), ‘Space-form and the acousmatic image’, Organised Sound, 12(1), pp. 35-58.

Upon successful completion of the course, students will be able to:

• Understand how computer networks function and are organized, and the use of applications over such networks
• Develop web applications that utilize audio data
• Understand the basic requirements of Networked Music Performance (NMP) systems
• Design and assemble a basic system for audio communication over a network
• Configure the parameters of audio communication systems according to the requirements of the use-case scenario

The course aims to help students acquire the following general competencies:

• Searching for, analyzing, and synthesizing data and information, using the necessary technologies
• Decision-making
• Project design and management
• Working autonomously and working in teams
• Promoting free, creative, and inductive thinking

This course covers fundamental knowledge in computer networking technology and focuses on techniques for audio signal transmission, particularly in web audio applications. Specifically, its content includes:

• Introduction to computer networks and the services they provide
• Layered network architecture (OSI Model)
• Distributed networks and network topologies
• Network nodes (routers, repeaters, bridges, firewalls, etc.)
• Internet protocols (TCP/IP Suite)
• Structure of network packets
• Routing, switching, multiplexing
• Network reliability and quality characteristics (bandwidth, transmission time, packet loss)
• Specifications of a Networked Music Performance system
• Current Networked Music Performance systems
• Encoding of audio signals for transmission
• Packet recovery techniques for reliable delivery
• Acoustic echo cancellation techniques
• Exercises in assembling a Networked Music Performance system
• Network simulation exercises
• Packet routing monitoring exercises

1. Tanenbaum Andrew, Δίκτυα Υπολογιστών, 4η Έκδοση, ΕΚΔΟΣΕΙΣ ΚΛΕΙΔΑΡΙΘΜΟΣ ΕΠΕ, 2011, η5 έκδοση
2. Kurose, Ross, Computer Networking, A Top-Down Approach 6th Edition
3. Gabrielli Leonardo, Squartini Stefano, Wireless Networked Music Performance, Springer Briefs in Electrical and Computer Engineering, 2016
4. Ciccarelli Faulkner, Δίκτυα Υπολογιστών Εισαγωγή στη Σύγχρονη Τεχνολογία, , Εκδόσεις Μ. Γκιούρδας, 2005
5. Rottondi, C., Chafe, C., Allocchio, C., & Sarti, A. (2016). An overview on networked music performance technologies. IEEE Access, 4, 8823–8843. https://doi.org/10.1109/ACCESS.2016.2628440
6. Alexandraki C. & Akoumianakis D. (2010): Exploring New Perspectives in Network Music Performance: The DIAMOUSES Framework, Computer Music Journal, 34(2): 66-83

Machine learning has become a fundamental component of numerous commercial and research applications. Using the Python programming language and libraries such as PyTorch, it is possible to rapidly develop sophisticated applications in areas including audio-based human–computer interaction (e.g., natural language dialogue systems), music information retrieval, and many others.

With this in mind, the course aims to introduce students to the field of machine learning. Within this framework, students will study the principles underlying the various stages involved in implementing data-driven knowledge discovery systems, using both classical machine learning techniques and state-of-the-art methods. The course also covers the latest approaches to natural language interaction based on large language models. Lectures will address the complete machine learning workflow, including data collection, feature extraction, model training, and performance evaluation.

In addition to providing the necessary theoretical background, the course makes extensive use of Python libraries that are widely employed in both research and commercial applications for the development of automatic pattern recognition systems in domains such as natural language processing, audio signal processing, and related fields.

Upon successful completion of the course, students will be able to:

Understand the fundamental concepts and applications of machine learning.

Evaluate the advantages and limitations of widely used machine learning algorithms.

Design datasets and construct reliable training and evaluation sets for data-driven knowledge discovery.

Apply advanced model evaluation techniques and hyperparameter optimization methods.

Demonstrate proficiency in the use of popular Python-based machine learning libraries and frameworks.

Employ trained large language models to develop natural language human–computer interaction systems.

The following topics will be covered in the Applied Machine Learning course.

Theory

Introduction to machine learning: linear regression and logistic classification

Handwritten digit classification from images: introduction to neural networks

Convolutional neural networks for image and audio processing; transposed (deconvolutional) neural networks

Autoencoders, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs)

Semantic embedding methods for time-series data

Time-series modeling and processing using Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks

Transformers and Large Language Models (LLMs)

Large Language Models for tool use, Retrieval-Augmented Generation (RAG), and Agentic AI

Laboratory Exercises

Forward propagation in neural networks

Handwritten digit classification

Convolutional neural networks for image and audio processing

Variational Autoencoders for audio applications

Semantic word embedding exercises for natural language

Using LSTM networks for music emotion recognition

Using Large Language Models for natural language interaction

Using Large Language Models and external tools for music analysis