英语翻译随着现代信息技术及大众传播媒体的迅猛发展,在生活中,人们借助网络对流行音乐的欣赏更加迅速和广泛.流行音乐是贴近大

英语翻译
随着现代信息技术及大众传播媒体的迅猛发展,在生活中,人们借助网络对流行音乐的欣赏更加迅速和广泛.流行音乐是贴近大众、是最容易引起人们内心共鸣的艺术.而不同年龄、经历的人喜欢不同风格的流行音乐,所以对流行音乐风格的合理划分以满足不同人群的需求是网络电台当前的首要任务.
解决对流行音乐风格分类的问题,就是要给出一个合理地分类方法正确识别不通过风格的语音信号.本文采用BP神经网络模型,将Pop(流行)、Country(乡村)、Jazz(爵士)、Rock(摇滚)、R&B(节奏布鲁斯)、New Age（新世纪）这六种流行音乐风格的语音特征信号作为神经网络的输入值.在隐含层进行算法迭代,利用归一化方法的最大最小值法,归一函数用MATLAB自带函数mapminmax,测试语音特征信号,得到BP神经网络分类误差（如图1-2）,BP神经网络分类正确率如下表.

With the rapid development of modern information technology and mass media, in life, people through the network to appreciate to the pop music is more rapid and extensive. Pop music is popular, is the most easy to arouse people inner resonance art. The different age, different experience of people like pop music, so the reasonable division of the popular music styles in order to meet the needs of different groups of people is a priority task network radio.
To solve the pop music style classification problem, is to give a reasonable classification recognition not through the style of speech signals. This paper uses the BP neural network model, the Pop (POP), Country (rural), Jazz (Jazz), Rock (rock), R&B (rhythm Bruce), New Age (New Century) speech signal characteristics of these six kinds of popular music styles as neural network input value. The algorithm iterations in the hidden layer, maximum and minimum values by using the method of normalization method normalization function, with its own MATLAB function mapminmax, test speech feature signal, obtain the BP neural network classification error, BP neural network classification accuracy as shown in the following table.