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Selection of Approximated Activation Function in Neural Network-Based Sound Classifiers for Digital Hearing Aids

机译：基于神经网络的数字助听器声音分类器中近似激活函数的选择

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摘要

The feasible implementation of signal processing techniques on hearing aids is constrained to the limited number of instructions per second to implement the algorithms on the digital signal processor the hearing aid is based on. This adversely limits the design of a neural network-based classifier embedded in the hearing aid. Aiming at helping the processor achieve accurate enough results, and in the effort of reducing the number of instructions per second, this paper focuses on exploring the most adequate approximations for the activation function. The experimental work proves that the approximated neural network-based classifier achieves the same efficiency as that reached by "exact" networks (without these approximations), but, this is the crucial point, with the added advantage of extremely reducing the computational cost on digital signal processor.
机译：助听器上信号处理技术的可行实施方式被限制为每秒有限的指令数量，以在助听器所基于的数字信号处理器上实现算法。这不利地限制了助听器中嵌入的基于神经网络的分类器的设计。为了帮助处理器获得足够准确的结果，并努力减少每秒的指令数量，本文着重探讨了激活函数的最适当近似值。实验工作证明，基于近似神经网络的分类器可以达到与“精确”网络（没有这些近似）所达到的效率相同的效率，但这是关键点，具有极大地降低数字计算成本的优势。信号处理器。

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