The course Music and Sound Effects combines theoretical content with practical activities focused on digital music production. It covers key aspects such as audio mixing, the use and editing of MIDI tools, basic harmony and composition, sound synthesis and design, the application of effects, and bus processing and mastering techniques. This conceptual framework is complemented by laboratory exercises and the development of group projects using advanced music production software, with the aim of applying the acquired knowledge in an integrated manner and becoming familiar with a working environment similar to that of a professional studio.
Titular Professors
Professors
Time and frequency characterization of analog signals and systems. Sampling Theorem. Complex numbers and series of functions
Learning Objectives of the Course
O1. To provide students with the essential foundations of digital music production, including the use of audio editors, MIDI tools, and basic mixing and mastering techniques.
O2. To introduce the principles of harmony, musical composition, and structural organisation of musical pieces, enabling students to understand and recognise the formal elements of modern music.
O3. To teach the basic concepts of sound synthesis, sound design, and timbre generation using digital methodologies and virtual instrumentation tools.
O4. To develop the competencies required to use professional music?production software in the creation of artistic projects, integrating recording, editing, and the creation of audio and MIDI tracks.
1 Characterization of discrete LTI systems. Impulse response
1.1 Introduction
1.2 The linear time invariant systems (LIT)
1.3 Impulse response and discrete linear convolution
1.4 Linear difference equations with constant coefficients
2 Fourier analysis for discrete signals and systems
2.1 Introduction to TFSD
2.2 Representation of frequency sequences by TFSD
2.3 Properties of TFSD
2.4 Digital processing of analog band limited signals
2.5 Changing the sampling frequency in the discrete domain (decimation and interpolation)
2.6 The Discrete Fourier Transform (D.F.T.)
2.7 Properties of D.F.T
2.8 Computation of linear convolution using DFT
3 The Z transform
3.1 Introduction
3.2 Definition of Z transform
3.3 Properties of the region of convergence (ROC)
3.4 Properties of Z transform
3.5 The inverse transform Z
3.6 Study of linear and invariant systems with the transformed Z
The subject has a weekly operation with 2 or 3 teaching sessions during which the contents of the topics are developed through lectures, flipped classroom and problem sessions. Within these sessions also some short lab-based exercises are proposed to be developed with the Matlab environment individually, which help the student to consolidate the theoretical knowledge through a practical and applied approach. At the end of the study of the main theoretical framework, a long lab-based exercise in Matlab environment is proposed that the students have to solve in groups, and make a final delivery. These activities are carried out partially during class, where they can resolve doubts with the teachers.
The final grade is calculated with:
Final Grade = 70% · Theory + 30% · Practice
You must obtain at least a 4 in each part to apply the formula. The subject is passed with a Final Grade ≥ 5.
Theory is evaluated through a checkpoint of topics 1 and 2, the January exam and the continuous assessment:
Theory Grade = max(Ex_Grade ; 80% · Ex_Grade + 20% · AC)
Practice is evaluated according to: Practice = 40% · Pr_Delivery + 60% · Pr_Exam
The Pr_Delivery includes code, memory and/or interview, and the Pr_Exam individually evaluates the practice carried out. The assessment activities will be governed by the current regulations on campus copies.
In the extraordinary call, the theory can be recovered, through a global exam, and the practices that have been failed or not submitted.
Use of AI
In the practicals, the use of AI tools, programming assistants or functionalities integrated in environments such as MATLAB is allowed, in accordance with Level 4 of the AI Assessment Scale (AIAS). The student is responsible for critically understanding all the work submitted. This understanding will be assessed individually in the Exam_pr and in the personal interview included in the Delivery_pr.
It will be assessed:
- The correct application of algebraic and mathematical methods in problem solving
- Rigour and coherence in the development of mathematical reasoning
- Conceptual understanding of the fundamentals of signal processing
- The ability to apply and adapt techniques to new contexts and problems involving the analysis and design of discrete?time signal processing systems
- Accuracy in calculations and correct interpretation of the obtained results
- Clarity and structure in the presentation of procedures and solutions
Course documentation on the course intranet. For each course topic: Class slides, Solved problems, Documents on properties, Theory documents with practical examples.
Notes on the course practices.
Examples of Matlab code on various concepts in the syllabus.
.
Oppenheim, Alan V; Schafer, Ronald W. Discrete-Time Signal Processing. Prentice-Hall. New Jersey, 1999
Rawat, Tarun Kumar. Signals and Systems. Oxford University Press, 2010