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继续往上传基音检测论文,希望对你有帮助.共同奋斗

于 2022-07-10 发布 文件大小:1.59 MB
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继续往上传基音检测论文,希望对你有帮助.共同奋斗-Continue to upload pitch detection papers, I hope you have help. The common struggle

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    This code is used to remove silence from a voice signal so that the recognition in voice recognition will give good result. The method is Scale on threshold applied to the envelope for detecting scilence periods. The actual threshold is computed by multiplying threshin" by the median value of the envelope samples. The default is (.25), whichis 25 of the median envelope value over input signal .-This code is used to remove silence from a voice signal so that the recognition in voice recognition will give good result. The method is Scale on threshold applied to the envelope for detecting scilence periods. The actual threshold is computed by multiplying threshin" by the median value of the envelope samples. The default is (.25), whichis 25 of the median envelope value over input signal .
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    积分:1
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    数字音频格式有很多种,其质量与采样频率和采样精度两个参数有关。频率的表示单位为赫兹〔Hz〕,它表示每秒采样次数。采样频率越高,音质就越好。采样精度为每次采样所存储的数据数量,它决定每个数字信号所能够表示的离散振幅的数量。存储每个样本的数据越多,音质就越好。但是高品质的声音需要占用大量的内存和磁盘空间。考虑到网络带宽,在Internet连接上传输就需要花费很长的时间。对于Applet来说,保证声音文件的最小化是极为重要的。-digital audio format there are many, the quality and frequency of sampling and sampling accuracy of the two parameters. The frequency of Hertz unit of [Hz], it said the number of samples per second. The higher sampling frequency, the sound quality better. For each sampling precision sampling by the amount of data storage, it was decided that each digital signal can be expressed as the number of discrete amplitude. Each sample storage of more data, better sound quality. But high-quality voice would take a lot of memory and disk space. Consideration of the network bandwidth, the transmission on the Internet connection on the need to spend a very long time. Applet for instance, to ensure that voices of the smallest document is very important.
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  • 的硕士论文
    首先对语音信号的预处理和特征提取问题进行了讨论,提取了三个有效的语音识别特征参数—LPC系数、LPC倒谱系数和Mel频率倒谱系数(MFCC);其次着熏讨论了语音识别中常用的两种识别方法—基于模板匹配的DTW和基于统计膜型的HMM在语音识别中的应用,分别讨论了各自的模型训练和识别的方 法,对HMM在实际应用中的一些具体问题也进行了讨论;最后对常规HMM提出了一些改进方法,包括显式状态驻留和随机分段模型(SM),重点讨论了SM在 语音识别中的训练和识别方法,并通过一个孤立数字语音识别系统验证了其比常规HMM更能描述语音信号的特征。
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