Seminar/Thesis: Automatic tagging audio databases

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Machine learning methods can nowadays be very helpful to automatically collect data and build large-sized datasets. However, the retrieved datasets need to be cataloged before being further processed. The project aims at implementing a convenient algorithm for the automatic tagging of audio data retrieved from publicly available repositories.


Apply algorithms to catalog and tag automatically audio data retrieved from publicly available repositories.

Learning objectives

  • Analyse audio data from open source repositories
  • Apply audio processing methods to segment and tag data stream
  • Apply machine learning algorithms to automate the process


Project type Seminar (optional: Master thesis)
ECTS 2.5, 5, 7.5, default: 5
Language English
Period Summer term 2021
Presence time Virtual seminar, working from remote
Useful knowledge Python, Machine Learning, Audio Processing
Work distribution 100% algorithm development
Med. Eng. designation Advanced Context Recognition (ACR)
StudOn link Please join
First Meeting online-introduction-vorbesprechung-of-summer-term-2021-seminars, on 12th April 2021 at 16:15
Registration Via StudOn, obligatory after introduction


Up-to-date literature recommendations are provided during the lectures.


Final presentation and final report.


Annalisa Baronetto

  • Job title: Researcher
  • Address:
    Henkestraße 91, Haus 7, 1. OG
    91052 Erlangen
  • Phone number: +49 9131 85 23608
  • Email:

Friedrich-Alexander-Universität Erlangen-Nürnberg