New: Audio Processing Using Python Laboratory

General Information

Tutors: Christoph Weyer, Till Hardenbicker

Date: 10 practice sessions à 4 hours
(further details are announced at the start of the semester)

Requirements: Completed Bachelor Degree

Manuscript: All needed documents are provided free of charge

Language: Englisch and German

Registration: via  RWTHonline


Introductory session (Mandatory):
Thursday, April 15th, 2021
02.00pm - 03.30pm

This semester, the lab will take place online via zoom on thursdays. If there are enough participants, a second time slot will be offered on Wednesdays. In total, the lab is limited to 24 participants. In the obligatory introductory session, it is decided who will participate in the lab. The link will be provided in advance for all registered students.

Lab time takes place on Thursdays from 2:00-6:00 p.m.

Lab 1: 22.04.2021
Lab 2: 29.04.2021
Lab 3: 06.05.2021
Lab 4: 20.05.2021
Lab 5: 10.06.2021
Lab 6: 17.06.2021
Lab 7: 24.06.2021
Lab 8: 01.07.2021
Lab 9: 08.07.2021
Lab 10: 15.07.2021



The programming language Python enables rapid and comprehensive development of prototypes for signal processing and machine learning. Powerful libraries are available since a few years. Because of the scope of possibilities and the open-source license, Python has found widespread use in research groups and departments in academia and industry. This module allows students to experience Python in order to prepare for working in science or industry.

This lab addresses two core targets: learning programming techniques in Python as well as applying fundamental techniques in signal processing and machine learning. Both targets are pursued in parallel in the context of audio signal processing; the Python libraries and the machine learning methods are oriented towards applications from this field. The lab is organized in a series of prepared experiments on diverse use cases, for instance:

  • Signal analysis
  • Filter design
  • Adaptive filters and noise reduction
  • Multi-channel and spatial audio processing
  • Pattern recognition using machine learning
  • Classification and regression techniques


Hier werden die Evaluierungsergebnisse der letzten Jahre für das Praktikum zusammengefasst.

Sommersemester 2021

Teilnehmer an der Evaluierung: 23
Globalindikator: 1,6
Konzept des Praktikums: 1,7
Vermittlung und Betreuung: 15