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Friday, October 29 • 9:00pm - Friday, December 3 • 5:45pm
Deep Learning for Audio Signal Processing, with Python and Pytorch Examples Tutorial

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In this tutorial, we will show some basic building blocks of deep learning, particularly for audio, from the perspective of signal processing. The idea is to show some similarities between familiar signal processing structures and deep learning architectures. For that, we use examples in Python and Pytorch.

We start with best practices for deep learning, then exploring convolutional neural networks as filter banks (analysis and synthesis) and autoencoders as a filter bank-based audio coder, and finally, we discuss recurrent neural networks as IIR (infinite impulse response) filters. This is done using audio examples and Python Pytorch program examples.


  • Best Practices for machine learning in audio
  • Specific properties of audio signals and typical features
  • Convolutional layers as filter banks
  • Autoencoders as Filter bank with optimization
  • Variational autoencoder as audio coder with quantization
  • Recurrent Neural Networks as Infinite Impulse Response filters

The Jupyter notebook file for the tutorial slides can be found at github.com/TUIlmenauAMS/AES_Tutorial_2021.

avatar for Renato de Castro Rabelo Profeta

Renato de Castro Rabelo Profeta

Technische Universität Ilmenau
Renato Profeta is a Ph.D. Candidate in Audio Signal Processing at the Ilmenau University of Technology.He received a Master of Engineering degree in Electrical Engineering from Kempten University of Applied Sciences and a Bachelor of Engineering in Electrical Engineering from Riga... Read More →

Sascha Spors

University of Rostock
avatar for Gerald Schuller

Gerald Schuller

Professor, Ilmenau University of Technology
Audio Coding, Machine Learning for Audio. Short Bio: Gerald Schuller is a full professor at the Institute for Media Technology of the Technical University of Ilmenau, since 2008. He was head of the Audio Coding for Special Applications group of the Fraunhofer Institute for Digital... Read More →

Friday October 29, 2021 9:00pm - Friday December 3, 2021 5:45pm EST