Lecture: Music Processing Analysis, Winter Term 2023/2024

01_MusicRepr_Teaser 02_FourierTr_Teaser 03_MusicSync_Teaser2 04_AudioStru_Teaser 06_Teaser_BeatTempo 07_Teaser_AudioRetr

  • Instructor: Prof. Dr. Meinard Müller
  • Tutor: Simon Schwär
  • Credits:
    • 2.5 ECTS (Lecture only)
    • 5 ECTS (Lecture with Exercises; only for selected study programmes)
  • Time (Lecture): Winter Term 2023/2024, Mo 16:15–18:00
  • Time (Exercises): Winter Term 2023/2024, Mo 14:20–15:50
  • Place: Am Wolfsmantel 33, Erlangen-Tennenlohe, Room 3R4.04
  • 1. Lecture: 16.10.2023
  • 1. Excercise: 23.10.2023
  • Note: There is no lecture/exercise on 06.11.2023.
  • Dates (Lecture): Mo 16.10.2023, Mo 23.10.2023, Mo 30.10.2023, Mo 06.11.2023, Mo 13.11.2023, Mo 20.11.2023, Mo 27.11.2023, Mo 04.12.2023, Mo 11.12.2023, Mo 18.12.2023, Mo 08.01.2024, Mo 15.01.2024, Mo 22.01.2024, Mo 29.01.2024, Mo 05.02.2024
Important Notes:
  • The lecture Music Processing Analysis will be offered as a fully in-person event. Attendance is only possible physically at the seminar room of the AudioLabs (Am Wolfsmantel 33, Erlangen-Tennenlohe, Room 3R4.04).
  • Please register for the course via StudOn prior to the first lecture. Please contact Simon Schwär for questions on StudOn.
  • Besides a traditional lecturing format, this course will also be inspired by the flipped classroom concept. Important elements are:
  • We recommend students to prepare before the lecture. The lecture time can then be used for deepening the most important aspects, and for having a question–answering dialogue with participants.
  • All FAU students can get an electronic copy of the required textbook via SpringerLink. To this end, you need to be logged in via an FAU account. It may be benefial to have a physical copy of the book to allow for offline reading. You may print out the required pages, or you may purchase a high-quality softcover edition at low cost (MyCopy softcover) following SpringerLink.
  • For each lecture unit, you find a separate website with the following material:


Music signals possess specific acoustic and structural characteristics that are not shared by spoken language or audio signals from other domains. In fact, many music analysis tasks only become feasible by exploiting suitable music-specific assumptions. In this course, we study feature design principles that have been applied to music signals to account for the music-specific aspects. In particular, we discuss various musically expressive feature representations that refer to musical dimensions such as harmony, rhythm, timbre, or melody. Furthermore, we highlight the practical and musical relevance of these feature representations in the context of current music analysis and retrieval tasks. Here, our general goal is to show how the development of music-specific signal processing techniques is of fundamental importance for tackling challenging music analysis problems.

The general research area of music processing covers a wide range of subfields and tasks such as music anaylsis, music synthesis, computer music composition, performance analysis, or audio coding not to speak from close connections to other disciplines such as musicology or library sciences. In this course, we present a selection of topics with an emphasis on music analysis and retrieval having a focus on audio recordings. Most of these topics are connected to the research area known as music information retrieval (MIR), which aims at developing computational tools for processing, searching, organizing, and accessing music-related data.

The lecture closely follows the textbook Fundamentals of Music Processing (FMP). Additionally, the FMP Notebooks offer a collection of educational material, providing detailed textbook-like explanations of central techniques and algorithms in combination with Python code examples that illustrate how to implement the theory.

The following video gives a brief impression about this course.

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Course Requirements

In this course, we discuss a number of current research problems in music processing and music information retrieval (MIR) covering aspects from information science and audio signal processing. While we provide the necessary background information, a good understanding of general concepts in signal processing and data science (e.g., algorithms, data structures) as well as strong mathematical background is required. Furthermore, good programming skills are a prerequisite for participating in the exercises. In particular, participants are required to experiment with the presented algorithms using Python and Jupyter notebooks. Furthermore, basic knowledge in music theory and a strong interest in music are extremely helpful to get enthusiastic about the field of music processing.


Accompanying Textbook and Notebooks


Meinard Müller
Fundamentals of Music Processing
Using Python and Jupyter Notebooks
2nd edition, 495 p., hardcover
ISBN: 978-3-030-69807-2
Springer, 2021
Accompanying Website

FMP Notebooks
Comprehensive framework based on Jupyter notebooks
for teaching and learning fundamentals of music processing.

Lecture: Topics, Material, Instructions, Questions & Answers

The lecture material includes textbook passages, notebooks, handouts of slides, videos, and so on. In the following list, you find detailed descriptions and links to the material. If you have any questions regarding the lecture, please contact Prof. Dr. Meinard Müller.

Exercises: Material, Instructions

The exercises, which are mainly offered to computer science, AI, and data science students, accompany and extend the lecture Music Processing Analysis. In the exercise meetings, we review the lecture, discuss homework problems, deal with programming issues, and realize mini-projects that implement basic algorithms and procedures. Note that good programming skills are a prerequisite for participating in the exercises. In particular, we assume basic knowledge in Python as covered by the PCP Notebooks. If you have any questions regarding the exercise, please contact Simon Schwär.

The exercise sessions start at 14:20 (until 15:50) in the same room as the lecture (3R4.04 in Erlangen-Tennenlohe).

Phase 1: Notebooks
Please complete the relevant tasks before the exercise session. You can download the MPAE notebooks here.

  • Exercise MPAE1: Music Representations
    • For the session on 23.10.2023: Tasks 0, 1 and 2
    • For the session on 30.10.2023: Tasks 3 and 4 (plus the optional task)
  • Exercise MPAE2: Fourier Transform
    • For the session on 13.11.2023: Tasks 1, 2, 3 and 4
    • For the session on 20.11.2023: Tasks 5 and 6
  • Lab Exams on STFT and HPSS on 27.11.2023
    In this session, you will be asked to present your solutions to the two lab notebooks on STFT and HPSS. You should work on this in pairs which we can organize in the first few exercise sessions. You can bring the solutions for presentation on your own computer or on a USB stick. The lab exams run with the FMP environment. You may ignore the questions masked as "Homework exercises". Do not hesitate to ask questions via mail or in the exercise sessions before 27.11.!

Phase 2: Projects

  • Brainstorming session for project work: 04.12.2023
    Bring your ideas for music processing projects you want to tackle in the rest of the semester! We will form groups during this session.
  • Introductory presentations for project work: 11.12.2023
    Present your goals and outline techniques you want to use. Maximum 4 minutes per group. You should use a Jupyter notebook for the presentation.
  • Mandatory reading assignment
    Due via mail to Simon on Sunday, 14.01.2024 - Each student must summarize a chapter from the FMP textbook using the provided Latex template (2 pages without references; do not copy text or figures from the book). These are individual submissions - do not work in groups.
  • Final presentations on projects and demo session: 29.01.2024
    2 minutes teaser-presentations per group, afterwards demo sessions.