Müller – Fundamentals of Music Processing

Argomenti del programma: Part 0: introduction to the course and analysis of the needs and requirements of a Computer Music system. Part 1: SuperCollider The SuperCollider ecosystem SuperCollider as a Computer Music Programming Language Practical sessions Part 2: Plugins Computer Music Languages: development of plugins for Digital Audio Workstations (Juce) Practical sessions Part 3: interaction design Machine to machine interaction: MIDI,…

Argomenti del programma: The course introduces the main data-driven approaches in audio, music, and acoustic engineering, including regression, classification, detection, source enhancement, dereverberation, and audio generation. We will cover: -Fundamentals of machine learning for audio signals: supervised and unsupervised learning; regression, classification, detection; training, validation, test, loss functions, gradient-based…

Argomenti del programma: Part 1: Introduction Course structure Goals of the course Overview of signal processing and machine learning in audio and acoustic engineering Historical perspective on machine learning for audio Challenges in machine learning for audio signal processing Main application areas, with emphasis on music and acoustic applications Development process of smart audio systems Part 2: Audio Analysis and Preprocessing Data…