Description: Speech analysis and parameter extraction Short-term analysis, frames and windows Time-domain analysis: energy, zero-crossings, statistic parameters, autocorrelation
Frequency-domain analysis: spectra and spectrograms Cepstral analysis Linear prediction analysis Pitch and formant estimation
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Author:栗子 |
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Description: 用Matlab以及c语言求语音序列的倒谱
这某段语音信号求FFT、取模、取log、IFFT得到倒谱，并画图。选取一段进行分析，分别对浊音和清音各自求倒谱。-Using Matlab, as well as for c language serial Cepstral voice that voice signal for a certain period of FFT, check mode, check log, IFFT been cepstrum, and drawing. Select a section of the analysis, respectively, and the voiceless to voiced their demand cepstrum. Platform: |
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Author:海德 |
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Description: 当把m个信号叠加在一起时，经倒谱分析是能检测出其基频。但倒谱分析主要是用在同态滤波中，可用于某些信号的解褶积。例如提供的语音信号，就能通过倒谱把频谱中的包络和基频频谱分离，-When the m signal superimposed together, by the cepstrum analysis is able to detect its fundamental frequency. However, cepstral analysis is mainly used in homomorphic filtering can be used for the solution of certain signal deconvolution. Such as the provision of the speech signal, we can through the spectrum of cepstral envelope and baseband frequency spectrum separation Platform: |
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Author:小单 |
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Description: Speech analysis and parameter extraction Short-term analysis, frames and windows Time-domain analysis: energy, zero-crossings, statistic parameters, autocorrelation
Frequency-domain analysis: spectra and spectrograms Cepstral analysis Linear prediction analysis Pitch and formant estimation
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Platform: |
Size: 2220032 |
Author:栗子 |
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Description: 求出语音信号的倒谱以便于进一步对语音信号作进一步的分析处理，分析信号的特性。-Derived Cepstral voice signal in order to facilitate further speech signal processing for further analysis, analysis of signal characteristics. Platform: |
Size: 2048 |
Author:zhandong |
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Description: 可以在CCS中运行的LPCC程序，包括语音参数分析主函数，信号的自相关函数，由自相关函数计算LPC预测系数，由LPC预测系数计算LPC倒谱系数，由LPC预测系数计算MEl到普系数等函数-CCS can be run at the LPCC procedures, including analysis of voice parameters of the main function, the signal autocorrelation function, autocorrelation function calculated from LPC prediction coefficients, by the LPC prediction coefficients LPC cepstral coefficients, calculated by the LPC prediction coefficients to the S & P coefficients Mel such as function Platform: |
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Author:renmay |
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Description: 本 文 首先 介绍了语音识别的研究和发展状况，然后循着语音识别系统的
处理过程，介绍了语音识别的各个步骤，并对每个步骤可用的几种方法在实
验基础上进行了分析对比。研究了语音信号的预处理和特征参数提取，包括
语音信号的数字化、分帧加窗、预加重滤波、端点检测及时域特征向量和变
换域特征向量.其中端点检测采用双门限法.通过实验比对特征参数的选取，
采用12阶线性预测倒谱系数作为识别参数。详细分析了特定人孤立词识别算
法，选定动态时间弯折为识别算法，并重点介绍其设计实现。
在 Vi su alC++环境下，设计并实现一个特定人、孤立词语音识别系统，
系统可以识别数字0-9等简单指令。该系统还具备演示、学习功能，可以演
示语音处理的各个步骤，还可以根据需要添加新的指令。
最 后 ， 重点从端点检测算法和动态时间弯折识别算法对系统进行改进。
实验表明，改进后的系统识别率有很大提高，达到95 ，为进一步开发实用
性语音识别系统产品打下了基础。-This article introduced the first speech recognition research and development, and then follow the voice recognition system
Processing, speech recognition, introduced the various steps, each step of the methods available in the real
A post-mortem conducted on the basis of the analysis and comparison. Research on the speech signal pre-processing and feature extraction, including
Digitized voice signals, sub-frame window, pre-emphasis filtering, endpoint detection feature vector in time domain and variable
Eigenvector for the domain. One endpoint detection method using dual-threshold. Through experiments over the selection of characteristic parameters,
The use of 12-order linear prediction cepstral coefficients as recognition parameters. Detailed analysis of the specific operator who isolated word recognition
Law, selected Dynamic Time Warping Algorithm for identifying and focusing on the achievement of its design.
In Vi su alC++ environment, design and realization of a s Platform: |
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Author:周文超 |
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Description: 本文利用人耳的感知特性,提出了加权倒谱系数,并建立 了相应的分析算法。-In this paper, characteristics of the human ears perception presented a weighted cepstral coefficients, and the establishment of a corresponding analysis algorithms. Platform: |
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Author:Martiney |
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Description: 提出了一种计算Mel频倒谱参数特征的改进算法, 该算法采用了加权滤波器分析技术来提高语音信号低频部分的频谱分辨率,使之更符合人类听觉系统的特性。-Proposed a method for calculating parameters of Mel-frequency cepstral features improve the algorithm, which uses a weighted filter analysis techniques to improve the voice signal low-frequency part of the spectrum resolution, to make it more in line with the characteristics of the human auditory system. Platform: |
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Author:Martiney |
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Description: 为了实现高速语音特征参数的提取，在分析了美尔频率倒谱特征参数提取算法的基础上，提出了算法的硬件
设计方案，介绍了各模块的设计原理。该方案增加了语音激活检测功能，可对语音信号中的噪音帧进行检测，提高了特征参
数的可靠性。-In order to achieve high-speed voice characteristic parameter extraction, in the analysis of Mel frequency cepstral feature extraction algorithm is proposed based on the algorithm the hardware
Design, describes the design principles of each module. The program increased the Voice Activity Detection feature, which allows voice signals to detect the noise frame to improve the characteristic parameters
The number of reliability. Platform: |
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Author:于高 |
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Description: 语音信号的时域频域分析，从短时能量到语谱图，以及线性预测参数和梅尔倒谱系数-Speech signal in time domain frequency domain analysis, from the short-term energy to the spectrogram, and the linear prediction parameters and the Mel cepstral coefficients, etc. Platform: |
Size: 747520 |
Author:菁菁 |
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Description: 频谱分析中的倒频谱分析,在各类故障诊断非常有效，本例子是专门针对声音文档，也可用于其他信号-Spectral analysis, cepstral analysis, is very effective in the diagnosis of various types of failures, the present example is dedicated for audio documents, can also be used for other signals Platform: |
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Author:的的的 |
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