Institute of Formal and Applied Linguistics

Wednesday 15:40, S9 (preliminary)

It's possible this will move if enough people agree.

Annotation (from SIS)

The subject introduces participants to computational music processing both in the industrial and academic areas: music representations, from audio to symbolic representations (MIDI, MusicXML) to the visual domain (sheet music), and methods from signal processing to machine learning. This subject is a good basis for music-related software projects or theses. Knowing music theory and notation is not required (the essentials will be explained), but if you never had any contact with music, we recommend reading up on terms like harmony or musical form. The subject will be taught in English.

Note on schedule: this is not a centrally scheduled subject, so scheduling will happen once we know who signed up (more or less democratically, depending on how many people are interested).

The subject's discord channel is already open -- you can join and ask for whatever you might want. (Joining the discord of course doesn't obligate you in any way to join the subject, it's there also to help you decide if you want to take it.)

Literature

Müller, Meinard. Fundamentals of Music Processing Using Python and Jupyter Notebooks. Cham: Springer, 2021.
https://link.springer.com/content/pdf/10.1007/978-3-030-69808-9.pdf

Müller, Meinard, and Frank Zalkow. "libfmp: A Python package for fundamentals of music processing." Journal of Open Source Software 6, no. 63 (2021): 3326.
https://joss.theoj.org/papers/10.21105/joss.03326.pdf

Lerch, Alexander. An Introduction to Audio Content Analysis: Music Information Retrieval Tasks and Applications. 2nd Edition. New York: Wiley-IEEE Press, 2021.
Freely available as slides: https://github.com/alexanderlerch/ACA-Slides
and accompanying code: https://github.com/alexanderlerch/pyACA
and website: https://www.audiocontentanalysis.org/

Knees, Peter, and Markus Schedl. Music similarity and retrieval: an introduction to audio-and web-based strategies. Vol. 36. Heidelberg: Springer, 2016.
https://link.springer.com/content/pdf/10.1007/978-3-662-49722-7.pdf

As you have probably noted, some of the instructional literature on computational music processing pre-dates the boom of deep learning. However, the methods presented there are often still valid and in many application scenarios good enough, and make for good baselines before wheeling out the deep learning artillery.

 

Syllabus

1. Music and its formalizations. Sound vs. tone. Elementary musical features (tempo, beat, harmony, melody). User roles (listener, distributor, musician).

2. Basics of musical audio processing: signal, sampling, convolution and the deconvolution problem. Resonance, harmonic row and timbre.

3. Audio feature extraction. Tones and pitches. Automated transcription of monophonic and polyphonic recordings, melody extraction, harmony and genre. Beat tracking, downbeat and tempo estimation. Source separation.

4. Symbolic music description. MIDI, matrix view. Music notation formats: ABC, humdrum, LilyPond, MusicXML, MEI. Selected databases of symbolic music.

5. Visual representations of music: notation. OMR worldwide and at FMP CUNI.

6. Musical similarity in symbolic representations and in audio. Search, mulitmodality — query by humming.

7. Multimodality and performance. Score following with and without symbolic representations and its applications. Modeling music expression. Automatic adaptive accompaniment.

8. Singing and lyrics. Singing voice detection, singing voice synthesis, automatic transcription and alignment of sung text.

9. Digital music history. Databases: RISM, F-Tempo, Cantus. Examples of digital editions (Mozart, Josquin), popular music databases (Billboard, Million Songs Dataset).

10. Music generation. Algorithmicity, chance, and generative artificial intelligence. Various human-in-the-loop systems.

11. Music distribution. Recommender systems — collaborative filtering, cold start problem. Copyright: ContentID, fingerprinting. Cover song identification.

12. Music cognition. EEG and music, entrainment, music therapy: Parkinson’s disease, depression, Alzheimer disease.

13. Non-European musical cultures. Ragas, Chinese music, Maqams, Arab-Andalusian music. Folk music. Cultural evolution perspectives.

14. The world of computational music processing: industry, academia, important online resources. Worldwide/Europe/Czechia/FMP CUNI. Improtant open-source libraries.

15. Recap for exam, reserve time, discussion space.

(The subject does not touch on digital music production and audio engineering: no live coding, no DAW work, no VST plugins, etc.)