吉林大学学报(信息科学版) ›› 2021, Vol. 39 ›› Issue (1): 60-65.

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FCNN 深度学习模型及其在动物语音识别中的应用

  

  1. 长春工业大学 数学与统计学院, 长春 130012
  • 收稿日期:2020-08-13 出版日期:2021-03-19 发布日期:2021-03-20
  • 通讯作者: 刘铭(1979— ), 男, 长春人, 长春工业大学教授, 硕士生导师,主要从事机器学习、 大数据分析与数据挖掘研究,(Tel)86-15843108878(E-mail)jlcclm@163.com
  • 作者简介:石鑫鑫(1993— ), 女, 辽宁阜新人, 长春工业大学硕士研究生, 主要从事机器学习与数据挖掘研究,(Tel)86-18143070716(E-mail)1946494479@qq.com
  • 基金资助:
    国家自然科学基金资助项目(61503150); 吉林省自然科学基金资助项目(2020021157JC); 吉林省教育厅科学技术基金资助项目(JJKH20191295KJ)

FCNN Deep Learning Model and Its Application in Animal Speech Recognition

  1. College of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
  • Received:2020-08-13 Online:2021-03-19 Published:2021-03-20

摘要: 为解决使用语音信号准确识别动物以保护和研究野生动物的问题, 提出一种全连接算法与稀疏连接算法相结合的全卷积神经网络(FCNN: Fully Convolutional Neural Network), 用于语音的自动识别。 利用全连接算法提取更多的组合特征, 稀疏连接算法筛选重要特征可加快收敛速度。 同时给出了具体的模型结构及算法流程,并进行了动物语音识别实验。 实验结果表明, 该全卷积神经网络深度学习算法是一种语音自动识别的有效方法, 解决了蛙声识别问题, 为动物语音识别提供参考。

关键词: 语音识别, 卷积神经网络, 全卷积神经网络

Abstract: In order to solve the problem of using voice signals to accurately identify animals so as to protect and research wild animals. We propose a FCNN (Fully Convolutional Neural Network) combining a fully connected algorithm and a sparse connection algorithm for automatic speech recognition. The fully connected algorithm is used to extract more combined features, and the sparse connection algorithm to select important features to speed up the convergence. The specific model structure and algorithm flow are given, and speech recognition experiments are carried out. The experimental results show that the fully convolutional neural network deep learning algorithm is an effective method for automatic speech recognition. It can solve the problem of frog sound recognition and provide a reference for animal speech recognition.

Key words: speech recognition, convolutional neural network, fully convolutional neural network

中图分类号: 

  • TP391. 41