Automated Labeling and Iterative Learning for Signals - MATLAB
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    Automated Labeling and Iterative Learning for Signals

    Overview

    Labeling signal data is very important step in creating AI-based signal processing solutions. However, this step can be very time-consuming and manual.

    In this session, we introduce signal labeling for use in AI applications and discuss how MATLAB can be used to speed up and  simplify the process. We describe the use of preprocessing to extract information from signals. The session will cover different approaches for signal labeling including using algorithms and automating with deep learning models. We will also discuss an iterative method of building deep learning models and reduce human effort in labeling.

    Highlights include:

    • Using and extending the Signal Labeler app
    • Preprocessing to facilitate signal labeling
    • Iteratively building and incorporating deep learning models
    • Automating signal labeling

    About the Presenter

    Esha Shah is a Product Manager at MathWorks focusing on Signal Processing and Wavelets Toolbox. She supports MATLAB users focusing on advanced signal processing and AI workflows. Before joining MathWorks, she received her Master’s in Engineering Management from Dartmouth College and Bachelor’s in Electronics and Telecommunication Engineering from Pune University, India.

    Recorded: 29 Jul 2021

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