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Description: Generalized Mel frequency cepstral coefficients for large-vocabulary Speaker-Independent Continuous-Speech Recognition 关于MFCC算法的很好的英语文章-Generalized Mel frequency cepstral coefficients for large-vocabulary Speaker-Independent Continuous-Speech Recognition on the MFCC algorithm is a very good article in English
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Size: 165888 |
Author: xiang |
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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.
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Size: 4096 |
Author: 海德 |
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Description: mfcc mel倒谱系数学习。适合语音识别参数的学习。-mfcc mel cepstral coefficient learning. Parameters for speech recognition learning.
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Size: 1024 |
Author: 沈立金 |
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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
to run the application please tape menu
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Size: 2220032 |
Author: 栗子 |
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Description: 语音信号分析软件，提取美尔倒谱系数等等功能-Speech signal analysis software, extract Simmel cepstral coefficient features, etc.
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Size: 964608 |
Author: xpwang |
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Description: matlab 求线性倒谱系数,用于语音识别-matlab linear cepstral coefficients for speech recognition
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Size: 424960 |
Author: 吴欢欢 |
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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.
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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
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Size: 3072 |
Author: renmay |
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Description: MFCC (Mel Frequent Cepstral Coefficient) in M-File.
epresentation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency.
MFCCs derived as follows:
1. Take the Fourier transform of (a windowed excerpt of) a signal.
2. Map the powers of the spectrum obtained above onto the mel scale, using triangular overlapping windows.
3. Take the logs of the powers at each of the mel frequencies.
4. Take the discrete cosine transform of the list of mel log powers, as if it were a signal.
5. The MFCCs are the amplitudes of the resulting spectrum.
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Size: 1024 |
Author: Mitha |
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Description: cepstral smoothing module and demo
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Size: 27648 |
Author: falcon |
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Description: 对语音信号进行倒谱分析，产生信号图形和倒谱图形-Voice signals on cepstral analysis, signal graphics and graphics cepstrum
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Size: 352256 |
Author: 赵树森 |
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Description: In this project we have processed the speech signal with the help of the DIGITAL SIGNAL PROCESSING techniques. The speech signal is given as the input will be verified using speech recognition technique using matlab. We have used Mel Frequency Cepstral Coefficient (MFCC) along with Vector Quantization (VQLBG) and Euclidean Distance to identify different characters. Based on the results, data was send to Parallel Printer Port of the computer & using relay different devices will be controlled.
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Size: 2048 |
Author: SimonKap22 |
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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.
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Size: 317440 |
Author: Martiney |
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Description: 从说话人的语音信号中提取说话人的个性特征是声纹识别的关键。主要介绍语音信号特征提取方法中的Mel倒谱系数
-From the speaker s voice signal to extract the speaker s personality traits is the key to Voiceprint identification. Mainly introduces the speech signal feature extraction method in Mel cepstral coefficients
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Size: 241664 |
Author: 于高 |
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Description: 提取LPC参数进行，倒谱系数的计算，用于静音检测算法，-Extract LPC parameters, the calculation of cepstral coefficients for mute detection algorithm
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Size: 428032 |
Author: 李鹏伟 |
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Description: Speech Recognition using MEL cepstral Coefficients
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Size: 3072 |
Author: ab.m |
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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.
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Size: 747520 |
Author: 菁菁 |
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Description: This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordings are divided into acoustically similar regions and classified into basic audio types such as speech, music or silence. Audio features used in this project include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate and Short Term Energy (STE). These features were extracted from audio files that were stored in a WAV format. Possible use of features, which are extracted directly from MPEG audio files, is also considered. Statistical based methods are used to segment and classify audio signals using these features. The classification methods used include the General Mixture Model (GMM) and the k- Nearest Neighbour (k-NN) algorithms. It is shown that the system implemented achieves an accuracy rate of more than 95 for discrete audio classification.-This project describes the work done on the development of an audio segmentation and classification system. Many existing works on audio classification deal with the problem of classifying known homogeneous audio segments. In this work, audio recordings are divided into acoustically similar regions and classified into basic audio types such as speech, music or silence. Audio features used in this project include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate and Short Term Energy (STE). These features were extracted from audio files that were stored in a WAV format. Possible use of features, which are extracted directly from MPEG audio files, is also considered. Statistical based methods are used to segment and classify audio signals using these features. The classification methods used include the General Mixture Model (GMM) and the k- Nearest Neighbour (k-NN) algorithms. It is shown that the system implemented achieves an accuracy rate of more than 95 for discrete audio classification.
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Size: 653312 |
Author: kvga |
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Description: 提取线性预测倒谱系数的matlab程序，使用的是自相关法-Linear prediction cepstral coefficients extracted the matlab program, using the autocorrelation
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Size: 1024 |
Author: zhupeng |
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Description: VQ声纹识别算法和实验.
摘要：采用线性预测倒谱系数(1inear prediction cepstrum coefficient，LPCC)作为语音的特征参数，矢量量化(vector quantity，VQ)方法进行模式匹配，探讨声纹识别以实现身份认证，并对此识别方法进行了相关的实验．通过验证，这种方法可以区分不同的说话人，并且在做说话人辨认实验时可达到较高的识别率．-VQ pattern recognition algorithms and experimental sound. Abstract: The linear prediction cepstral coefficients (1inear prediction cepstrum coefficient, LPCC) as the voice of the characteristic parameters, vector quantization (vector quantity, VQ) method of pattern matching, pattern recognition of sound in order to achieve authentication and identification methods relevant to this experiment. Validated, this method can distinguish between different speaker, and speaker identification experiments to do to reach a higher recognition rate.
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Size: 171008 |
Author: 海边贝壳 |
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