Mou, A.; Milanova, M. Performance Analysis of Deep Learning Model-Compression Techniques for Audio Classification on Edge Devices. Sci2024, 6, 21.
Mou, A.; Milanova, M. Performance Analysis of Deep Learning Model-Compression Techniques for Audio Classification on Edge Devices. Sci 2024, 6, 21.
Mou, A.; Milanova, M. Performance Analysis of Deep Learning Model-Compression Techniques for Audio Classification on Edge Devices. Sci2024, 6, 21.
Mou, A.; Milanova, M. Performance Analysis of Deep Learning Model-Compression Techniques for Audio Classification on Edge Devices. Sci 2024, 6, 21.
Abstract
Audio classification using deep learning models, essential for applications like voice assistants and music analysis, faces challenges when deployed on edge devices due to their limited computational resources and memory. Achieving a balance between performance, efficiency, and accuracy is a significant obstacle in optimizing these models for such constrained environments. In this investigation, we evaluate diverse deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), for audio classification tasks on the ESC 50, UrbanSound8k, and Audio Set datasets. Our empirical findings indicate that Mel Spectrograms outperform raw audio data, attributing this enhancement to their synergistic alignment with advanced image classification algorithms and their congruence with human auditory perception. To address the constraints of model size, we apply model compression techniques, notably magnitude pruning, Taylor Pruning, and 8-bit quantization. The research demonstrates that a hybrid pruned model achieves a commendable accuracy rate of 89 percent, which, although marginally lower than the 92 percent accuracy of the uncompressed CNN, strikingly illustrates an equilibrium between efficiency and performance. Subsequently, we deploy the optimized model on Raspberry Pi 4 and NVIDIA Jetson Nano platforms for audio classification tasks. These findings highlight the significant potential of model compression strategies in enabling effective deep learning applications on resource-limited devices, with minimal compromise on accuracy.
Keywords
model compression; deep learning; audio classification; LSTM; CNN; edge Device
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
