Seminar/Thesis: Transfer learning strategies to classify audio datasets

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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 catalogued before being further processed. Classification may require expert knowledge annotation to train the algorithm, and the process may be tedious if done manually. The project aims at implementing a convenient transfer learning technique to automatically train algorithms for the tagging of audio datasets.


Apply transfer learning techniques to train algorithms automatically to catalogue and tag audio datasets.

Learning objectives

  • Analyse audio data from open source repositories
  • Apply transfer learning methods to train classification algorithm
  • Apply machine learning algorithms to tag audio data automatically


Project typeSeminar (optional: Master thesis)
ECTS2.5, 5, 7.5, default: 5
PeriodWinter term 2021-22
Presence timeVirtual seminar, working from remote
Useful knowledgePython, Machine learning
Work distribution100% algorithm development
Med. Eng. designationAdvanced Context Recognition (ACR)
StudOn linkPlease join
First meetingCDH Seminar and Thesis Introduction, 18th October 16:00
RegistrationVia 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