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 Platform: |
Size: 165888 |
Author:xiang |
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Description: 建立了一种基于自组织神经网络的语音识别系统。对语音信号进行了预处理, 提取了语音信号的线性预测系数、线性预测倒谱系数和Mel 倒谱特征系数, 建立了基于自组织神经网络的识别判决模型.-Established a self-organizing neural network-based speech recognition system. Carried out on the speech signal pre-processing, extraction of the speech signal linear prediction coefficient, linear prediction cepstral coefficients and the characteristics of Mel cepstrum coefficient, based on self-organizing neural network model of identification judgments. Platform: |
Size: 114688 |
Author:张小天 |
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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: 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 Platform: |
Size: 241664 |
Author:于高 |
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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: 这个函数也要被melcepst函数调用，用来进行计算Mel倒谱系数。这个函数的作用是构造mel滤波器组。-This function must be melcepst function call is used to calculate Mel cepstral coefficients. The role of this function is to construct a mel filter bank. Platform: |
Size: 2048 |
Author:bradden |
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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: 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. Platform: |
Size: 653312 |
Author:kvga |
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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的关键。主要介绍语音信号特征提取方法中的Mel倒谱系数
-Extracted from the speaker' s voice signal in the speaker' s personality is the key to the voiceprint identification. Introduces the speech signal feature extraction method in Mel cepstral coefficients Platform: |
Size: 241664 |
Author:tb |
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Search - mel cepstral coefficients