A DNN Compression Method for Environmental Sound Classification on Microcontroller Unit

doi:10.3969/j.issn.1006-2475.2024.01.013

Abstract

Abstract: Abstract： Environmental Sound Classification （ESC） is known as one of the most important topics of the non-speech audio classification task. In recent years， deep neural networks （DNNs） have made a lot of progress in ESC. However， DNNs are computationally and memory-intensive， and cannot be directly deployed on IoT devices based on microcontroller units （MCU）. To address this problem， this paper proposes a DNN compression method for highly resource-constrained devices. Since DNNs have a large number of parameters， which cannot be directly deployed， so this paper proposes to use the pruning method for substantial compression. Afterwards， aiming at the problem of accuracy loss caused by this operation， we design a knowledge distillation based on the feature information of multiple intermediate layers. Tests are carried out on public datasets （UrbanSound8K， ESC-50） using the STM32F746ZG device. The experimental results demonstrate that proposed method can achieve up to 97% compression rate while maintaining good inference performance and speed.

Key words: Key words： environmental sound classification, edge computing, microcontroller unit, pruning, knowledge distillation, quantization

CLC Number:

TP81

MENG Na, FANG Wei-wei, LU Hong-ying. A DNN Compression Method for Environmental Sound Classification on Microcontroller Unit[J]. Computer and Modernization, 2024, 0(01): 80-86.

References

［1］ NANNI L， MAGUOLO G， BRAHNAM S， et al. An ensemble of convolutional neural networks for audio classification［J］. Applied Sciences， 2021，11（13）. DOI： 10.3390/
app11135796.
［2］ DEMIR F， TURKOGLU M， ASLAN M， et al. A new pyramidal concatenated CNN approach for environmental sound classification［J］. Applied Acoustics， 2020，170. DOI：10.
1016/j.apacoust.2020.107520.
［3］ DAVIS N， SURESH K. Environmental sound classification using deep convolutional neural networks and data augmentation［C］// 2018 IEEE Recent Advances in Intelligent Computational Systems （RAICS）. 2018：41-45.
［4］ NORDBY J. Environmental sound classification on microcontrollers using convolutional neural networks［D］. Norwegian University of Life Sciences， 2019.
［5］ SZE V， CHEN Y H， YANG T J， et al. Efficient processing of deep neural networks： A tutorial and survey［J］. Proceedings of the IEEE， 2017，105（12）：2295-2329.
［6］ SHARMA J， GRANMO O C， GOODWIN M. Environment sound classification using multiple feature channels and attention based deep convolutional neural network［C］// Interspeech 2020. 2020：1186-1190.
［7］ ABDOLI S， CARDINAL P， KOERICH A L. End-to-end environmental sound classification using a 1D convolutional neural network［J］. Expert Systems with Applications， 2019，136：252-263.
［8］ PALANISAMY K， SINGHANIA D， YAO A. Rethinking CNN models for audio classification［J］. arXiv preprint arXiv：2007.11154， 2020.
［9］ LIN J， CHEN W M， LIN Y J， et al. Mcunet： Tiny deep learning on IoT devices［J］. Advances in Neural Information Processing Systems， 2020，33：11711-11722.
［10］ DOYU H， MORABITO R， HOLLER J. Bringing machine learning to the deepest IoT edge with TinyML as-a-service［J］. IEEE IoT Newsletter， 2020，11：1-3.
［11］ MOHAIMENUZZAMAN M， BERGMEIR C， WEST I， et al. Environmental sound classification on the edge： A pipeline for deep acoustic networks on extremely resource-constrained devices［J］. Pattern Recognition， 2023，133. DOI： 10.1016/j.patcog.2022.109025.
［12］ KUMARI S， ROY D， CARTWRIGHT M， et al. EdgeL3： Compressing L3-Net for mote scale urban noise monitoring［C］// 2019 IEEE International Parallel and Distributed Processing Symposium Workshops （IPDPSW）. 2019：877-884.
［13］ CERUTTI G， PRASAD R， BRUTTI A， et al. Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms［J］. IEEE Journal of Selected Topics in Signal Processing， 2020，14（4）：654-664.
［14］ SALAMON J， JACOBY C， BELLO J P. A dataset and taxonomy for urban sound research［C］// Proceedings of the 22nd ACM International Conference on Multimedia. 2014：1041-1044.
［15］ PICZAK K J. ESC： Dataset for environmental sound classification［C］// Proceedings of the 23rd ACM International Conference on Multimedia. 2015：1015-1018.
［16］ CHENG J， WANG P S， LI G， et al. Recent advances in efficient computation of deep convolutional neural networks［J］. Frontiers of Information Technology & Electronic Engineering， 2018，19（1）：64-77.
［17］ LOUIZOS C， WELLING M， KINGMA D P. Learning sparse neural networks through L_0 regularization［C］// International Conference on Learning Representations. 2018. DOI： 10.48550/arXiv.1712.01312.
［18］ LIU N， MA X L， XU Z Y， et al. Autocompress： An automatic DNN structured pruning framework for ultra-high compression rates［J］. Proceedings of the AAAI Conference on Artificial Intelligence， 2020，34（4）：4876-4883.
［19］ LIU M R， FANG W W， MA X D， et al. Channel pruning guided by spatial and channel attention for DNNs in intelligent edge computing［J］. Applied Soft Computing， 2021，110. DOI： 10.1016/j.asoc.2021.107636.
［20］ DUGGAL R， XIAO C， VUDUC R， et al. Cup： Cluster pruning for compressing deep neural networks［C］// 2021 IEEE International Conference on Big Data （Big Data）. 2021：5102-5106.
［21］ HINTON G， VINYALS O， DEAN J. Distilling the knowledge in a neural network［J］. arXiv preprint arXiv：1503.02531， 2015.
［22］ ROMERO A， BALLAS N， KAHOU S E， et al. Fitnets： Hints for thin deep nets［J］. arXiv preprint arXiv：1412.6550， 2015.
［23］ ZAGORUYKO S， KOMODAKIS N. Paying more attention to attention：Improving the performance of convolutional neural networks via attention transfer［J］. arXiv preprint arXiv：1612.03928， 2017.
［24］ WANG H， LOHIT S， JONES M， et al. Multi-head knowledge distillation for model compression［J］. arXiv preprint arXiv：2012.02911， 2020.
［25］ YANG C G， AN Z L， CAI L H， et al. Hierarchical self-supervised augmented knowledge distillation［C］// Proceedings of the 30th International Joint Conference on Artificial Intelligence. 2021：1217-1223.
［26］ CHEN D F， MEI J P， ZHANG Y， et al. Cross-layer distillation with semantic calibration［J］. Proceedings of the AAAI Conference on Artificial Intelligence， 2021，35（8）：7028-7036.
［27］ PASSBAN P， WU Y M， REZAGHOLIZADEH M， et al. ALP-KD： Attention-based layer projection for knowledge distillation［J］. Proceedings of the AAAI Conference on Artificial Intelligence， 2021，35（15）：13657-13665.
［28］ WU Y， REZAGHOLIZADEH M， GHADDAR A， et al. Universal-KD： Attention-based output-grounded intermediate layer knowledge distillation［C］// Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021：7649-7661.
［29］ GHOLAMI A， KIM S， DONG Z， et al. A survey of quantization methods for efficient neural network inference［M］// Low-Power Computer Vision. Chapman and Hall/CRC. 2022：291-326.
［30］ RASTEGARI M， ORDONEZ V， REDMON J， et al. Xnor-net： Imagenet classification using binary convolutional neural networks［C］// European Conference on Computer Vision. 2016：525-542.
［31］ COURBARIAUX M， BENGIO Y， DAVID J P. Binaryconnect： Training deep neural networks with binary weights during propagations［J］. Advances in Neural Information Processing Systems， 2015，28：12-19.
［32］ CHOI J， VENKATARAMANI S， SRINIVASAN V V， et al. Accurate and efficient 2-bit quantized neural networks［M］// Proceedings of Machine Learning and Systems. 2019：348-359.
［33］ CHOUKROUN Y， KRAVCHIK E， YANG F， et al. Low-bit quantization of neural networks for efficient inference［C］// 2019 IEEE/CVF International Conference on Computer Vision Workshop （ICCVW）. 2019：3009-3018.
［34］ BANNER R， NAHSHAN Y， SOUDRY D. Post training 4-bit quantization of convolutional networks for rapid-deployment［C］// Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019：7950-7958.
［35］ JACOB B， KLIGYS S， CHEN B， et al. Quantization and training of neural networks for efficient integer-arithmetic-only inference［C］// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018：2704-2713.
［36］ LIANG T L， GLOSSNER J， WANG L， et al. Pruning and quantization for deep neural network acceleration： A survey［J］. Neurocomputing， 2021，461：370-403.
［37］ DAVID R， DUKE J， JAIN A， et al. Tensorflow lite micro： Embedded machine learning for TinyML systems［J］. Proceedings of Machine Learning and Systems. 2021，3：800-811.

[1]	SHI Xingyu1, LI Qiang2, ZHUANG Li3, LIANG Yi3, WANG Qiulin3, CHEN Kai3, WU Chenzhou3, CHANG Sheng1. Object Detection Models Distillation Technique for Industrial Deployment [J]. Computer and Modernization, 2024, 0(10): 93-99.
[2]	LI Shuang1, 2, YE Ning1, 2, XU Kang1, 2, WANG Su1, WANG Ruchuan1, 2. Edge Computing Unloading Method for Intelligent Elderly Care [J]. Computer and Modernization, 2024, 0(06): 95-102.
[3]	CHEN Qi, LI Jing-jing. Computational Offloading Strategy Based on Multi-objective Optimization in D2D Network [J]. Computer and Modernization, 2024, 0(01): 21-28.
[4]	YAN Yang, ZHAN Zi-jun, CAO Shao-hua. Collaborative Device-based Large-scale Offloading: A Bi-level Optimization Algorithm Fusing Divide-and-conquer and Greedy [J]. Computer and Modernization, 2023, 0(11): 13-21.
[5]	HE Yu-peng, TAO Yong, WANG Bing-heng, ZHAO Ying-nan. Research Status and Prospect of Edge Computing in Smart Distribution Network [J]. Computer and Modernization, 2023, 0(08): 87-92.
[6]	CHEN Gang, WANG Zhi-jian, XU Sheng-chao. Mobile Edge Computing Task Offloading Based on Feasible Point Tracking Continuous#br# Convex Approximation [J]. Computer and Modernization, 2023, 0(08): 93-97.
[7]	CHEN Zhuo, QIAO Gui-fang, CHAI Xin-bo, DU Yi-jun, SHEN Chong-lin, WANG Yuan-hao. Multi-weather Vehicle Detection Algorithm Based on Modified Knowledge Distillation [J]. Computer and Modernization, 2023, 0(02): 50-57.
[8]	ZHANG Hao, LU Hong-ying. Byzantine Fault-tolerant Distributed Consistency Algorithm for Edge Computing Applications [J]. Computer and Modernization, 2022, 0(12): 33-41.
[9]	YAO Zheng. Privacy Protection Model for Medical Data Based on HISPAC [J]. Computer and Modernization, 2022, 0(09): 1-12.
[10]	LI Yi, WEI Jian-guo, LIU Guan-wei. Survey of Model Pruning Algorithms [J]. Computer and Modernization, 2022, 0(09): 51-59.
[11]	ZHAN Jun-wei, ZHUANG Yi. Mobile Edge Computing Task Offloading Model and Algorithm Based on Energy Consumption and Delay Optimization [J]. Computer and Modernization, 2022, 0(08): 86-93.
[12]	ZHANG Yan-hu, YAN Li-juan, MA Zhi-fen, ZHANG Yan-jun. An improved Particle Swarm Optimization (PSO) Force Unloading Algorithm for Multi-task and Multi-resource Moving Edge Computing Environment [J]. Computer and Modernization, 2022, 0(05): 54-60.
[13]	FENG Jing-xiang. Channel Pruning of Convolutional Neural Network Based on Transfer Learning [J]. Computer and Modernization, 2021, 0(12): 13-18.
[14]	WU Jian-bo, ZHU Wen-xia, JU Liang, XU Zhi-fang. Application of Edge Computing in Intelligent Transportation Systems [J]. Computer and Modernization, 2021, 0(12): 103-109.
[15]	WANG Shao-qiang, WANG Yu. New Data Processing System Based on Edge Computing [J]. Computer and Modernization, 2019, 0(07): 15-.