Sound Recognition System Using Spiking and MLP Neural Networks

被引：5

作者：

Cerezuela-Escudero, Elena ^{[1
]}

Jimenez-Fernandez, Angel ^{[1
]}

Paz-Vicente, Rafael ^{[1
]}

Dominguez-Morales, Juan P. ^{[1
]}

Dominguez-Morales, Manuel J. ^{[1
]}

Linares-Barranco, Alejandro ^{[1
]}

机构：

[1] Univ Seville, Dept Architecture & Technol Comp, Robot & Technol Comp Lab, Seville, Spain

来源：

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT II | 2016年 / 9887卷

关键词：

Neuromorphic auditory hardware; Address-Event representation; Spiking neural networks; Sound recognition; Spike signal processing; MODEL;

D O I：

10.1007/978-3-319-44781-0_43

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we explore the capabilities of a sound classification system that combines a Neuromorphic Auditory System for feature extraction and an artificial neural network for classification. Two models of neural network have been used: Multilayer Perceptron Neural Network and Spiking Neural Network. To compare their accuracies, both networks have been developed and trained to recognize pure tones in presence of white noise. The spiking neural network has been implemented in a FPGA device. The neuromorphic auditory system that is used in this work produces a form of representation that is analogous to the spike outputs of the biological cochlea. Both systems are able to distinguish the different sounds even in the presence of white noise. The recognition system based in a spiking neural networks has better accuracy, above 91 %, even when the sound has white noise with the same power.

引用

页码：363 / 371

页数：9

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