摘要
播音信号识别对于提高播音质量具有十分重要的意义,当前播音信号识别方法存在误识率高,播音信号识别效率低等不足,为了获得更优的播音信号识别结果,设计基于模糊神经网络的播音信号识别技术。首先分析当前播音信号识别技术的研究进展,指出各种播音信号识别技术存在的不足;然后采用空间变换和奇异值分解算法提取播音信号识别特征,并采用模糊神经网络建立播音信号识别分类器;最后在Matlab 2017平台上进行播音信号识别仿真实验,结果表明,模糊神经网络获得了理想的播音信号识别,播音信号识别正确率要高于当前其他播音信号识别技术,减少了播音信号的误识率,缩短了播音信号识别的时间,提升了播音信号识别速度,具有较高的实际应用价值。
Broadcasting signal recognition is of great significance to improve the quality of broadcasting.At present,there are some shortcomings in the methods of broadcasting signal recognition,such as high misrecognition rate and low recognition efficiency.In order to obtain better results of broadcasting signal recognition,a broadcasting signal recognition technology based on fuzzy neural network is designed.Firstly,the research progress of the current broadcasting signal recognition technologies is analyzed,and the shortcomings of various broadcasting signal recognition technologies are pointed out.Then,the spatial transformation and singular value decomposition algorithm are used to extract the recognition features of broadcasting signals,and the fuzzy neural network is used to establish the recognition classifier of broadcasting signals.Finally,the simulation experiment of the broadcasting signals is performed on the platform of Matlab 2017.The simulation results show that the broadcast signal recognition technology based on fuzzy neural network achieves ideal recognition of broadcasting signals,and its correct recognition rate of broadcasting signals is higher than that of other current recognition technologies.It reduces the error recognition rate of broadcasting signals,shortens the recognition time of broadcasting signals,and improves the recognition speed of broadcasting signals,which proves that it has a high practical application value.
作者
何艳萍
HE Yanping(School of Literature and Journalism,Baoji University of Arts and Sciences,Baoji 721013,China)
出处
《现代电子技术》
北大核心
2020年第3期54-57,共4页
Modern Electronics Technique
基金
宝鸡文理学院科研计划项目：新时期方言节目的创新思考（ZK2018077）
关键词
播音信号
信号识别
模糊神经网络
奇异值分解算法
识别特征
识别速度
broadcast signal
signal recognition
fuzzy neural network
singular value decomposition algorithm
recognition feature
recognition speed
