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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Evaluating the Performance of Pre-Trained Convolutional Neural Network for Audio Classification on Embedded Systems

Mimoun Lamrini^1,2, Mohamed Yassin Chkouri², Abdellah Touhafi^1,3

¹Department of Engineering Sciences and Technology (INDI), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium.

Sensors (Basel, Switzerland)

|July 14, 2023

Summary

This summary is machine-generated.

This study evaluates pre-trained models for environmental sound recognition on embedded devices like Raspberry Pi. Results show effective transfer learning, enabling efficient real-time applications in smart cities.

Keywords:

deep learning embedded system environment sound recognition pre-trained models

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Environmental Sound Recognition (ESR) is vital for smart cities, utilizing Machine Learning (ML) classifiers for audio categorization.
Deploying deep learning (DL) models on resource-constrained embedded devices presents significant challenges.

Purpose of the Study:

To evaluate an existing pre-trained model for Environmental Sound Recognition (ESR) deployment on Raspberry Pi (RPi) and Tensor Processing Units (TPUs).
To explore the impact of retraining parameters on sound classification performance across diverse datasets.

Main Methods:

An existing pre-trained DL model was evaluated for deployment on RPi and TPU platforms.
Sound classification performance was compared across three datasets (ESC-10, BDLib, Urban Sound) on laptops, RPi, and RPi with Coral TPU.
The influence of retraining parameters was investigated.

Main Results:

Accuracy rates on laptops reached up to 99% (ESC-10: 96.6%, BDLib: 100%, Urban Sound: 99%).
On RPi, accuracies were 96.4% (ESC-10), 100% (BDLib), and 95.3% (Urban Sound).
On RPi with Coral TPU, accuracies were 95.7% (ESC-10), 100% (BDLib), and 95.4% (Urban Sound).

Conclusions:

Pre-trained models are effective for transfer learning in embedded systems for ESR.
Utilizing pre-trained models reduces computational needs, facilitating faster inference and real-time applications.
This approach accelerates the development, deployment, and performance of embedded AI solutions.