||In this session, we present the main goals, structure and grading conditions of the Audio and Music Processing Lab course.
||Martín Haro and Dmitry Bogdanov
||In this hands-on session we will introduce the basic characteristics of the Python programming language. Python is an interpreted programming language that allows you to do almost anything possible with a compiled language (C/C++/Fortran) without requiring all the complexity: Automatic garbage collection, Dynamic typing, Interpreted and interactive, Object-oriented, “Batteries Included”, Free, Portable, Easy to Learn and Use, Truly Modular.
||Essentia is an audio analysis tool that allows you to easily extract features from an audio signal and develop new ones suited to your taste. In this session we will examine how to work interactively in a python session with Essentia and extract a few musical descriptors.
||Gaia is a generic similarity search engine, which is mostly used for music similarity. In this session we will use some features extracted from audio files during the previous session about Essentia to implement a very simple genre classification algorithm.
||In the first Canoris session we will look at the general theory of web applications and APIs and I will give a short overview of the service oriented architecture that powers Canoris. We will also discuss why these technologies are important to music technology. The session ends with some small assignments to learn how to interact with Canoris from Python.
||In this second Canoris session we will move on to some bigger tasks and try to build a small application.
||Freesound.org is a massive repository of audio clips contributed by users under a CC license. The new web API makes it possible to interact with this database from a remote client. We will practice the basics of web-based audio hacking through an applied project.
||Learn basics of programming VST plug-ins in C++. We will go step-by-step through making 1) a gain effect, 2) a sine wave oscillator and 3) a resonant low-pass filter. Homework assignment is making a ping-pong delay effect. You can use classroom PC or own laptop (requires MSVC or XCode to be installed). Bring headphones.
||Weka is a collection of machine learning algorithms for data mining tasks. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. In this session, we focus on the use of Weka and how a general MIR problem should be addressed.
||In this session, we focus on the Experimenter application included in Weka to address multiple experiments and determine the relevance of different parameters of each one.
||This last session is reserved to those students who want to present one of their assignments to all the students. Feedback from all the students is expected.