Page 39 - Kỷ yếu hội thảo khoa học lần thứ 12 - Công nghệ thông tin và Ứng dụng trong các lĩnh vực (CITA 2023)

Dinh-Hoang-Long Tran, Quoc-Huy Le 23

Implementation of Convolutional Neural Network on

a Microcontroller for Classification of Audio Signals

*
Dinh-Hoang-Long Tran, Quoc-Huy Le
Faculty of Advanced Science and Technology, The University of Danang -
University of Science and Technology, Danang 550000, Vietnam.
* lqhuy@dut.udn.vn

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.

Keywords: Audio Classification, Deep Learning, Convolutional Neural
Networks, Edge AI, Audio Signal Processing.

1 Introduction

Sound signals are valuable source of information [1][2]. Physically, sound is a
mechanical movement in a continuous medium, each vibration produces energy with
its characteristics based on characteristics such as frequency, and amplitude to identify
the source emitting it. Every action, every movement, and the phenomenon around our
environment creates vibrations from simple actions such as walking and talking. We
can build a system that makes good use of sound to detect dangerous events, e.g. when
a robber threatens a staff or infiltrator tries break the window. Using deep learning we
can have a model that could classify incoming sound to detect that sound is safe or
suspicious, thereby detecting a dangerous event [3][4][5][6]. This model can be
then embedded to a microcontroller to build a system for automatic detection and
classification of sounds [7][8][9]. The idea of this work is to build such a system that
can be used in warning system as illustrated in Fig. 1.

ISBN: 978-604-80-8083-9 CITA 2023