February 14 2019

Signal Processing for Deep Learning and Machine Learning

Machine learning and Deep Learning are powerful tools for solving complex modeling problems across a broad range of industries. The benefits of these techniques are being realised in applications everywhere, including predictive maintenance, health monitoring, financial portfolio forecasting, and advanced driver assistance systems.

However, developing predictive models using Deep Networks for signals obtained from sensors is not a trivial task. Moreover, there is an increasing need for developing smart sensor signal processing algorithms which can be either deployed on edge nodes / embedded devices or on the cloud depending on the application.

In this session we will showcase latest techniques in MATLAB including Invariant Wavelet Scattering Framework and how this technique can be used as an automatic feature extractor for building models that can classify signals. We will explore an example of classification system that is built using automatic feature extraction using invariant scattering networks with the goal of recognising the genre of a music sample.  We will also explore how capabilities in addon tools like Statistics and Machine Learning Toolbox and Deep Learning Toolbox can aid in building the predictive models.

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