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Analysis of fast prototyping of microcontroller-based ML software for acoustic signal classification


[ 1 ] Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ 2 ] Instytut Automatyki i Robotyki, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ S ] student | [ P ] employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics, electrical engineering and space technology

Year of publication


Chapter type

chapter in monograph / paper

Publication language


  • TinyML
  • Edge Impulse
  • Nordic Thingy
  • STM32
  • ArduinoNano 33

EN The paper analyzes the preparation of software for acoustic signal classification with machine learning techniques for microcontrollers. The design process was tested for three types of devices: Nordic Thingy:53, and Arduino Nano 33 BLE Sense Lite. The classifier training process was carried out using the Edge Impulse platform. Experimental studies were carried out for the process of classifying sound signals generated by the vacuum cleaner motor. The results of the training and the model test were presented for different configurations.

Pages (from - to)

36 - 41



SPA 2023 Signal Processing : Algorithms, Architectures, Arrangements, and Applications : Conference Proceedings, Poznan, 20th-22nd September 2023

Presented on

SPA 2023 26th IEEE Signal Processing - Algorithms, Architectures, Arrangements, and Applications, 20-22.09.2023, Poznań, Polska

Ministry points / chapter


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