Acoustic Event Detection and Classification

Seminar Type: - None -
Project Status: Open

 

Acoustic event classification and detection are important for many real-world applications such as ambient assisted living, security surveillance, meeting room transcription, human-computer interaction, and multimedia retrieval. In this work, the aim is to investigate novel machine learning techniques for acoustic event classification and detection. In particular, deep neural networks are of interest.

 We offer:

  • existing code of generative neural network models
  • ready-to-use HMM for integration

 Your Tasks:

  • simulate generative neural network model in python on the GPU using THEANO [1]
  • analyze the implemented systems in terms of accuracy and performance
  • contribute to scientific work in form of a paper

Your Outcome:

  • learn to implement and simulate very fast Neural Networks on a GPU
  • learn how to solve a difficult speech processing task
  • get a broad education in applied machine learning

Your Profile:

  • motivation and reliability are a prerequisite
  • good knowledge in machine learning and neural networks (at least 2 machine learning courses)
  • knowledge in python programming

Additional Information:

This thesis project is planned for a duration of 6 months starting immediately. 

Contact:

Franz Pernkopf (pernkopf@tugraz.at)

Matthias Zoehrer (matthias.zoehrer@tugraz.at or +43 (316) 873 - 4385)

References

[1] J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D.Warde-

Farley, and Y. Bengio, “Theano: a CPU and GPU math expression compiler,” in Proceedings of
the Python for Scientific Computing Conference (SciPy), Jun. 2010, oral Presentation.
Signal Processing and Speech Communication Laboratory (SPSC), Graz University of Technology, Inffeldgasse 16c, 8010 Graz, Austria, 
http://www.spsc.tugraz.at