Lecture: Music Processing Analysis, Winter Term 2018/2019

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

  • Instructor: Prof. Dr. Meinard Müller
  • Tutor: Frank Zalkow
  • Credits:
    • 2.5 ECTS (Lecture only)
    • 5 ECTS (Lecture with Exercises; only for Computer Science Students)
  • Time (Lecture): Winter Term 2018/2019, Mo 16:10–17:45
  • Time (Exercises): Winter Term 2018/2019, Mo 14:20–15:50
  • Place: Am Wolfsmantel 33, Erlangen-Tennenlohe, Room 3R4.04
  • 1. Lecture: 15.10.2018
  • 1. Excercise: 22.10.2018
  • Exam (graded): Oral examination at the end of term
  • Flyer: PDF
  • Dates (Lecture) (16:10–17:45, Room 3R4.04): Mo 15.10.2018, Mo 22.10.2018, Mo 29.10.2018, Mo 05.11.2018, Mo 12.11.2018, Mo 19.11.2018, Mo 26.11.2018, Mo 03.12.2018, Mo 10.12.2018, Mo 17.12.2018, Mo 07.01.2019, Mo 14.01.2019, Mo 21.01.2019
  • Examination Dates (Room 3R4.03): To be announced


The lecture has the following format:

  • Every meeting consists of 90 minutes
  • There will be additional exercises for computer science students. Details will be announced later.

For further information, please contact Prof. Dr. Meinard Müller.


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 otherwise infeasible music analysis problems.

The following video gives a brief impression about this course.

Course requirements

In this course, we discuss a number of current research problems in music processing or music information retrieval (MIR) covering aspects from information science and digital signal processing. We provide the necessary background information and give numerous motivating examples so that no specialized knowledge is required. However, the students should have a solid mathematical background. The lecture is accompanied by readings from textbooks or the research literature. Furthermore, the students are required to experiment with the presented algorithms using Python and/or MATLAB.


The general area of Music Processing covers a wide range of subfields and tasks such as music anaylsis, music synthesis, music information retrieval, 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.

The course "Music Processing Analysis" is closely related to the course Music Processing - Synthesis by Prof. Rudolf Rabenstein. The two courses complement each other, but can also be taken separately.

Further audio-related courses offered by the AudioLabs can be found at:

Accompanying Textbook


Meinard Müller
Fundamentals of Music Processing
Audio, Analysis, Algorithms, Applications
ISBN: 978-3-319-21944-8
Springer, 2015

Exercises (for Computer Science Students)

The exercises, which particulary provided for computer 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. If you have any questions regarding the exercise, please contact Frank Zalkow.

Exercise Meeting: 22.10.2018 (14:20–15:50)
  • Organization and announcements
  • Introduction to Python and Jupyter Notebook
  • Introduction of practical exercises (Due: 19.11.2018)
    • Practical Exercise 1: Short-Time Fourier Transform (Jupyter)
    • Practical Exercise 2: Harmonic-Percussive Source Separation (Jupyter)
    • Each group has to hand in solutions and Python implementations of the practical exercises which are to be presented in the meeting on 19.11.2018.
  • Homework:
Exercise Meeting: 19.11.2018 (14:20–15:50)
  • Presentation of practical exercises
Exercise Meeting: 26.11.2018 (14:20–15:50)
  • Allocation of reading assignments (Due: 7.1.2019)
Exercise Meeting: 7.1.2019 (14:20–15:50)
  • Start with Course Projects (Due: 21.1.2019)
Exercise Meeting: 21.1.2019 (14:20–15:50)
  • Presentation of Course Projects


  • Introduction
    Slides (PDF), Handouts (6 slides per page) (PDF)
  • Overview
    Slides (PDF), Handouts (6 slides per page) (PDF)
  • Music Representations
    Slides (PDF), Handouts (6 slides per page) (PDF)
    Literature: Chapter 1
  • Audio Features (Fourier Transform, Spectrogram, Pitch, Chroma)
    Slides (PDF), Handouts (6 slides per page) (PDF)
    Handwritten Notes (Fourier Transform as Optimization Problem) (PDF)
    Literature: Section 2.1, Section 3.1
  • Music Synchronization (Dynamic Time Warping)
    Slides (PDF), Handouts (6 slides per page) (PDF)
    Literature: Section 3.2, Section 3.3
  • Music Structure Analysis
    Slides (PDF), Handouts (6 slides per page) (PDF)
    Literature: Chapter 4
  • Harmony Analysis
    Slides (PDF), Handouts (6 slides per page) (PDF)
    Literature: Chapter 5
  • Tempo and Beat Tracking
    Slides (PDF), Handouts (6 slides per page) (PDF)
    Literature: Chapter 6
  • Audio Retrieval
    Slides (PDF), Handouts (6 slides per page) (PDF)
    Literature: Chapter 7
  • Audio Decomposition
    Slides (PDF), Handouts (6 slides per page) (PDF)