Implementation of Convolutional Neural Network on a Microcontroller for Classification of Audio Signals

Abstract

The goal of this work is to develop a compact and low-cost device to detect dangerous and suspicious sounds in a sensitive area. The proposed solution uses an STM32 microcontroller embedded with a deep learning model and equipped with various peripherals. For the demo purpose we used the STM32F746NGH6 Discovery Kit and built the convolutional neural network embedded in this microcontroller with the popular Keras API. We illustrated that the deep learning model using convolutional neural networks algorithm can be implemented on STM32F746NGH6 microcontroller kit and the device can classify the labeled audio with an accuracy of about 97%. We also found that signals from real sound sensors or real microphones have noises which affects strongly on the model accuracy and thus it is necessary to build the dataset based on the available hardware. Our in-progress work is to record audio using ADMP401 analog MEMS microphone available on the STM32F746NGH6 microcontroller and then using these datasets for a second experimental model.