Course Description

This course explores the interdisciplinary field of Music Informatics with a focus on the musical applications of AI. Students will learn computational methods for representing, analyzing, and processing music-related data, associating them with each other in various musical contexts. The curriculum first covers the fundamentals of music analysis tasks, including automatic music transcription, rhythm and chord analysis, music structure analysis, music source separation, music alignment, and music tagging and captioning. Building on these foundations, the course examines retrieval tasks such as audio fingerprinting, version identification, cover song detection, and music recommendation. Furthermore, the course addresses performance analysis within the contexts of live concerts and music education, as well as recent advances in music generation, focusing on the evaluation of generative models using various metrics. Through a combination of theoretical lectures and hands-on projects, students will gain practical experience with state-of-the-art computational tools.

Textbooks

• Fundamentals of Music Processing (FMP), Meinard Müller (2nd edition)
  (code: Python Notebooks for Fundamentals of Music Processing )
• Audio Content Analysis, Alexander Lerch (2nd edition)

KLMS             Classum             GitHub (Code)             Resources

Schedule

Week	Topics
1	Course Introduction [pdf] Music Data and Representations [pdf] Suggested Readings The FMP book (Chapter 1: Music Representations) [python notebook] The FMP book (Chapter 2: Fourier Analysis of Signals) [python notebook]
2	Music Data and Representations (Continued) Audio Feature Analysis [pdf] [practice] 01. audio representations.ipynb [practice] 02. audio features.ipynb
3	Audio Feature Analysis (Continued) Pitch Estimation [pdf] Quiz #1 Week 1-2 slides (Music Data and Representations, Audio Feature Analysis) FMP Book Chapter 1 and 2 (including the Python notebooks) [practice] 03. pitch estimation.ipynb
4	Pitch Estimation (Continued) Automatic Music Transcription [pdf] HW #1: Piano Music Transcription (due: Apr 9, 11:59PM) [link] (15% of the total score)
5	Automatic Music Transcription (Continued) Audio-to-Score Alignment [pdf] [practice] 04. automatic music transcription.ipynb [practice] 05. audio-to-score alignment.ipynb Suggested Readings The FMP book (Chapter 3: Music Synchronization) [python notebook]
6	Chord Recognition [pdf] Suggested Readings The FMP book (Chapter 5: Chord Recognition) [python notebook]
7	Tempo Estimation and Beat Tracking [pdf] Suggested Readings The FMP book (Chapter 6: Tempo and Beat Tracking) [python notebook] HW #2: Beat Tracking and Chord Recognition (due: May 3, 11:59PM) [link] (15% of the total score)
8	Midterm Week
9	[Apr-28] No Class (Conference Trip) Music Tagging and Retrieval [pdf]
10	[May-5] No Class (Children's Day) Music Tagging and Retrieval (Continued)
11	Music Representation Learning [pdf] HW #3: Music Tagging and Retrieval (due: May 24, 11:59PM) [link] (15% of the total score)
12	Music LLM (Invited Talk) [pdf] Symbolic Music Processing [pdf]
13	Music Source Separation [pdf] Paper Review
14	Paper Review
15	Final Project Progress Presentation (Oral)
16	Final Project Presentation (Poster)