Automated Labeling and Iterative Learning for Signals_21Oct21-1


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


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Date:  21 Oct 2021


14:00 - 15:00 (TH, VN, ID)

15:00 - 16:00 (SG, MY, PH)


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Nestor Fernandez

Nestor Fernandez works as an application engineer at TechSource and he focuses on areas of data analytics for applications involving time-series data. Nestor Fernandez has extensive research and industrial experience in developing signal processing and AI workflows. He received a Master’s Degree in Applied Science from the National University of Kaohsiung, Taiwan after finishing his Bachelor’s Degree in Applied Physics from UP Diliman.

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