Leveraging Deep Learning for Signal Processing Applications
Channel: Systematics MATLAB
Date: 2019-06-05

Kirthi Devleker, MathWorks gives a
 session at MATLAB Expo Israel 2019 focuses on the new approaches / architectures for performing Deep Learning on signals in MATLAB using latest features. The goal of the session is to showcase how easy it is to build a deep learning predictive model on signal data and then use the model to make live predictions and we will do so with real examples. The first example covers an approach to build a Modulation Detection model with the help of realistic simulated data using Deep Networks and then use the model to make live predictions on real wireless signals captured using Software Defined radio connected to MATLAB. Topics covered include how to generate data with impairments, process the I-Q samples, create and build a deep network model from scratch. In the second example we will look into how one can leverage Invariant Scattering Convolutional Networks and Deep Networks like LSTMs to build predictive models on signals. We will show how you can use these techniques to build a classifier that can identify the genre of a music sample and we will make predictions on new music samples. The techniques covered in this session can be applicable to any signals / timeseries data.


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