Description: 采用间接方法提取MEL倒谱，先计算自相关系数，然后由自相关系数计算LPC预测系数和反射系数，再计算LPC倒谱系数，最后由LPC倒谱系数计算MFCC 。-Indirect method of extracting MEL cepstrum, first calculating the autocorrelation coefficient, and then auto-correlation coefficient calculated by the LPC prediction coefficient and reflection coefficient, and then calculating the LPC cepstral coefficients, and finally by the LPC cepstral coefficient calculating MFCC. Platform: |
Size: 4114432 |
Author:大夹馅 |
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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: |
Size: 3072 |
Author:renmay |
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Description: OTOMATİ K KONUŞ MA TANIMA ALGORİ TMALARININ UYGULAMALARI
Kö ksal Ö CAL
Ankara Ü niversitesi
Fen Bilimleri Enstitüsü
Elektronik Mühendisliğ i Anabilim Dalı
Danı ş man : Yrd. Doç . Dr. H. Gö khan İ LK
Bu ç alı ş mada, SMM (Saklı Markov Model) tabanlı izole bir kelime tanı ma sistemi geliş tirilerek, sesin akustik parametreleri LPC (Linear Predictive Coding), LPCC (LPC Cepstrum), CEPS (Ayrı k Fourier dö nüş ümü tabanlı cepstrum) ve MFCC (Mel Frequency Cepstral Coefficients) ‘nin konuş macı dan bağ ı msı z konuş ma tanı ma sistemlerindeki performansları değ erlendirilmiş tir. Değ iş ik akustik parametrelerle birlikte değ iş ik S-OTOMATİ K KONUŞ MA TANIMA ALGORİ TMALARININ UYGULAMALARI
Kö ksal Ö CAL
Ankara Ü niversitesi
Fen Bilimleri Enstitüsü
Elektronik Mühendisliğ i Anabilim Dalı
Danı ş man : Yrd. Doç . Dr. H. Gö khan İ LK
Bu ç alı ş mada, SMM (Saklı Markov Model) tabanlı izole bir kelime tanı ma sistemi geliş tirilerek, sesin akustik parametreleri LPC (Linear Predictive Coding), LPCC (LPC Cepstrum), CEPS (Ayrı k Fourier dö nüş ümü tabanlı cepstrum) ve MFCC (Mel Frequency Cepstral Coefficients) ‘nin konuş macı dan bağ ı msı z konuş ma tanı ma sistemlerindeki performansları değ erlendirilmiş tir. Değ iş ik akustik parametrelerle birlikte değ iş ik SMM Platform: |
Size: 1241088 |
Author:strike |
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Description: LPC倒谱系数提取，提取出来的LPC倒谱系数可以线性预测，帮助语音信号处理-LPC cepstral coefficients the number of extraction，Extracted from the LPC cepstrum coefficients linear prediction to help the speech signal processing Platform: |
Size: 2048 |
Author:张晓阁 |
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Description: these are two audio features , LPC whith cepstral coefficients this a feature for speech recognition and speaker recognition , and other feature named zcr for calculate the change of the sing ( + or - ) of the signal. Platform: |
Size: 122880 |
Author:kato |
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Description: these are two audio features , LPC whith cepstral coefficients this a feature for speech recognition and speaker recognition , and other feature named zcr for calculate the change of the sing ( + or - ) of the signal. Platform: |
Size: 122880 |
Author:kato |
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Description: these are two audio features , LPC whith cepstral coefficients this a feature for speech recognition and speaker recognition , and other feature named zcr for calculate the change of the sing ( + or - ) of the signal. Platform: |
Size: 122880 |
Author:kato |
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Description: 这个MATLAB构建一个锻炼LPC声码器,即,执行LPC分析和合成语音文件,导致合成语音近似原始的演讲。LPC分析使用一个标准的自相关分析来确定LPC系数的设置,一帧一帧的基础上,以及框架获得。一个独立的分析方法(cepstral螺距内检测器)把每一帧的言论是要么表示演讲(时间由cepstral峰值的位置在指定范围的音调时期)或无声的言论(模拟随机噪声帧)0帧基音周期的样本。独立的分析提供了一个两国并存的激发函数LPC合成处理的一部分,包括一系列的脉冲(表示帧期间)和/或噪声序列(在无声的帧)。-This MATLAB exercise builds an LPC vocoder, i.e., performs LPC analysis and synthesis on a speech file, resulting in a synthetic speech approximation to the original speech. The LPC analysis uses a standard autocorrelation analysis to determine the sets of LPC coefficients, on a frame-by-frame basis, along with the frame-based gain. An independent analysis method (a cepstral pitch period detector) classifies each frame of speech as being either voiced speech (with period determined by the location of the cepstral peak in a designated range of pitch periods) or unvoiced speech (simulated by a random noise frame) with a frame pitch period of 0 samples. The independent analysis provides a two-state excitation function for the LPC synthesis part of the processing, consisting of a series of pitch pulses (during voiced frames) and/or noise sequences (during unvoiced frames). The file 5.13 LPC Vocoder.pdf provides a User s Guide for this exercise. Platform: |
Size: 2496512 |
Author:wujin |
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