Dec 6 2018

Deep Learning for Signals and Sound

Deep learning networks are proving to be versatile tools. Originally intended for image classification, they are increasingly being applied to a wide variety of other data types. In this webinar, we will explore deep learning fundamentals which provide the basis to understand and use deep neural networks for signal data. Through two examples, you will see deep learning in action, providing the ability to perform complex analyses of large data sets without being a domain expert.

Explore how MATLAB addresses the common challenges encountered using CNNs and LSTMs to create systems for signals and sound, and see new capabilities for deep learning for signal data.


We will demonstrate deep learning to denoise speech signals, and generate musical tunes.  You will see how you can use MATLAB to:

  • Train a neural network from scratch using LSTM and CNN network architectures
  • Use spectrograms and wavelets to create 3d representations of signals
  • Access, explore, and manipulate large amounts of data
  • Use GPUs to train neural networks faster

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