School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beersheba 8400711, Israel.
DSP Group LTD., Herzliya 4659071, Israel.
Sensors (Basel). 2021 Feb 4;21(4):1065. doi: 10.3390/s21041065.
This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron's characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.
这项工作提出了一种基于尖峰神经网络的新方法，用于声音预处理和分类。所提出的方法受到生物神经元使用尖峰神经元和基于尖峰时间依赖性可塑性（STDP）的学习规则的特征的启发。我们提出了一种基于尖峰神经网络（SNN）的生物上合理的声音分类框架，用于检测声信号中包含的嵌入频率。这项工作还展示了基于低功耗尖峰连续时间神经元（SCTN）的 SNN 网络的高效硬件实现。所提出的声音分类框架建议使用声学传感器与基于 SCTN 的网络进行直接脉冲密度调制（PDM）接口，避免使用昂贵的数模转换。本文提出了一种新的连接方法，应用于基于尖峰神经元（SN）的神经网络。我们建议将 SCTN 神经元视为可编程模拟电子电路设计中的基本构建块。通常，神经元在任何神经网络结构中用作重复的模块化元素，并且位于不同层的神经元之间的连接是明确定义的。因此，可以生成由具有全连接或部分连接的几个层组成的模块化神经网络结构。所提出的方法建议控制尖峰神经元的行为，并应用智能连接，以实现基于 SNN 的简单模拟电路的设计。与使用模拟电路和模数转换进行预处理阶段的现有基于 NN 的解决方案不同，我们建议将预处理阶段集成到网络中。这种方法允许将基本的 SCTN 视为模拟模块，从而能够设计基于 SNN 的简单模拟电路，神经元之间具有独特的互连。通过实施基于 SCTN 的谐振器进行声音特征提取和分类，证明了所提出方法的有效性。所提出的基于 SCTN 的声音分类方法在使用真实世界计算伙伴关系（RWCP）数据库时，分类准确率达到 98.73%。
