**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 2017/2018, Mo 16-18**Time (Exercises):**Mo 14-16**Place:**Am Wolfsmantel 33, Erlangen-Tennenlohe, Room 3R4.04**1. Lecture:**30.10.2017**1. Excercise:**06.11.2017**Exam (graded)**: Oral examination at the end of term**Flyer:**PDF**Dates (Lecture)**(, Room 3R4.04): Mo 30.10.2017, Mo 06.11.2017, Mo 13.11.2017, Mo 20.11.2017, Mo 27.11.2017, Mo 04.12.2017, Mo 11.12.2017, Mo 18.12.2017, Mo 08.01.2018, Mo 15.01.2018, Mo 22.01.2018, Mo 29.01.2018, Mo 05.02.2018**16:10 - 17:50****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.

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 MATLAB and/or Python.

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:

Meinard Müller

Fundamentals of Music Processing

Audio, Analysis, Algorithms, Applications

ISBN: 978-3-319-21944-8

Springer, 2015

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.

- Organization and announcements
- Introduction to Python, Jupyter Notebook, and/or MATLAB
- Introduction of practical exercises
**(Due: 27.11.2017)**- Practical Exercise 1: Short-Time Fourier Transform (Instructions, Sources, Jupyter)
- Practical Exercise 2: Harmonic-Percussive Source Separation (Instructions, Sources, Jupyter)
- Each group has to hand in solutions and Python/MATLAB implementations of the practical exercises which are to be presented in the meeting on 27.11.2017.

**Homework 1 (Due: 13.11.2017):**- Work through Python/MATLAB introduction, e.g. using the script "Preparation Course MATLAB Programming"
- Text Book: Ex. 1.5, Ex. 1.6, Ex. 1.8

- Discussion of Homework 1
- Complex numbers (Jupyter)
**Homework 2 (Due: 20.11.2017):**- Text Book: Ex. 2.1, Ex. 2.2, Ex. 2.5, Ex. 2.12, Ex. 2.14

- Discussion of Homework 2
- Fourier analysis (Jupyter)
**Homework 3 (Due: 27.11.2017):**- Text Book: Ex. 2.3, Ex. 2.4, Ex. 2.15

- Discussion of Homework 3
- Continuation of Fourier analysis notebook.
- Homework 4: /
- Discussion of practical exercises STFT and HPSS.

- Continuation of discussion of practical exercises STFT and HPSS.
**Homework 5 (Due: 11.12.2017):**- Text Book: Ex. 3.3, Ex. 3.4, Ex. 3.5, Ex. 3.6

**Reading Assignment + Summary (mandatory, Due: 08.01.2018):**- Choose one of the following chapters from the Text Book: 4, 5, 6, 7, 8
- Each group has to hand in a summary (mandatory) of the assigned book chapter as a PDF until
**08.01.2017**. Please send the PDF via e-mail to Prof. Dr. Meinard Müller and Frank Zalkow. - Written in English, min. 2 pages
**Latex sources for the summary's template (with additional explanations) can be found here: ZIP**

- Discussion of Homework 5
- Continuation of discussion of practical exercises HPSS.
**Homework: Think of possible Course Project (Due: 18.12.2017)****Homework 6 (Due: 18.12.2017):**- Text Book: Ex. 3.7, Ex. 3.8, Ex. 3.10, Ex. 3.13

- Discussion and Fixing Course Projects
- The topic should be related to the lecture or Text Book.
**Course Project (Due: 29.01.2018)**- Write a small program to approach your idea.
- Prepare a short presentation (2 minutes, StudOn material should be used).
- Prepare a demo running on your own computer.
- Document in our StudOn Wiki (in the style of the
*example-project*).

- Discussion of Course Projects
- First demos
- Documentation

- Presentation of your Student 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)