Environmental Sound Classification(ESC)is known as one of the most important topics of the non-speech audio clas-sification task.In recent years,deep neural networks(DNNs)have made a lot of progress in ESC.However,DNNs are computa-tionally and memory-intensive,and cannot be directly deployed on IoT devices based on microcontroller units(MCU).To ad-dress 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%com-pression rate while maintaining good inference performance and speed.
维普
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