Blog Post

Recognize Voice and Audio with the TI SimpleLink CC1352P

Machine LearningEdge Impulse adds seamless audio and voice recognition capabilities to the SimpleLink™ CC1352P wireless MCU LaunchPad™ development kit

David Schwarz

November 10, 2021

Recently, Edge Impulse and Texas Instruments collaborated to bring embedded machine learning to low-cost and ultra-low-power wireless devices. Now we are excited to announce that Edge Impulse fully supports the TI Audio BoosterPack plug-in module, adding seamless audio and voice recognition capabilities to the SimpleLink™ CC1352P wireless MCU LaunchPad™ development kit.

This Audio BoosterPack adds a microphone to the LaunchPad, and with it you can rapidly prototype new audio processing applications using Edge Impulse and TI. Control your smart home network with your voice or recognize sounds of glass breaking, hydraulic shocks, and manufacturing defects. All this is now possible on a low-cost, battery-operated sensor with multiprotocol wireless connectivity!

Read the How to get started? sectionHow to get started?

Build your smart audio sensing product for the TI CC1352P wireless MCU today with our getting started guide

  1. Purchase the CC1352P1 LaunchPad kit, and the CC3200AUDBOOST audio booster pack
  2. Configure and connect the board to Edge Impulse
  3. Follow the Edge Impulse tutorial on Recognizing sounds from audio
  4. Go to the Deployment tab of your Edge Impulse project, then build and download a ready-to-go binary that includes your machine learning model for the LaunchPad; or deploy as a C++ library and integrate the model into your own firmware!
Deploy trained audio models directly to the CC1352P LaunchPad

Read the Next steps sectionNext steps

To add audio recognition capabilities to your own application or SimpleLink™ device, head to our documentation to learn how you can easily integrate Edge Impulse with our standalone firmware example

To learn more about embedded machine learning, watch our Hackster.io webinar for a deep dive into designing environmental sensing applications and detecting the sounds of glass breaking with embedded machine learning.

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