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9MATLABCHULIXIN
- 第9章共振峰的估算方法259 9.1预加重和端点检测259 9.1.1预加重259 9.1.2端点检测260 9.2倒谱法对共振峰的估算260 9.2.1倒谱法共振峰估算的原理260 9.2.2倒谱法共振峰估算的MATLAB程序261 9.3LPC法对共振峰的估算262 9.3.1LPC法共振峰估算的原理262 9.3.2LPC内插法共振峰的估算263 9.3.3LPC求根法共振峰的估算266 9.4连续语音LPC法共振峰的检测268 9.4.1简
3MATLABYUYIN
- 3.1语音信号的同态处理和倒谱分析30 3.1.1同态处理的基本原理30 3.1.2复倒谱和倒谱31 3.2离散余弦变换34 3.3Mel频率倒谱系数的分析37 3.3.1Mel滤波器组37 3.3.2MFCC特征参数提取38 3.4小波和小波包变换43 3.4.1小波变换43 3.4.2小波包变换44 3.4.3小波包算法45 3.4.4MATLAB中一维小波和小波包变换函数46 3.4.5MATLAB语音信号小波和小波包变换的例子49 3.
speech-analysis
- 对语音进行分析，包括时域分析（包括能量、过零率、互相关函数）和频域分析（包括fft变换、倒谱、LPC）-Speech analysis, including time domain analysis (including energy, zero-crossing rate, the cross-correlation function) and frequency domain analysis (including fft transform, cepstrum, LPC)
mfcc
- matlab版本，提取语音梅尔频谱倒谱系数（MFCC）特征-Matlab version, Mel frequency cepstrum coefficient（MFCC） feature extraction of speech
stft
- 里面包括短时傅里叶变换谱，倒谱平滑等，这些代码广泛用于，语音说话人识别，语音增强，雷达，声呐，图像等领域-Including short-time Fourier transform spectrum, cepstrum smooth, etc., are widely used in the code, voice speaker recognition, speech enhancement, radar, sonar, image, etc
quhunxiangDCT
- 运用复倒谱的方法,对信号进行去混响处理,将混响有效去除,被称为DCT法。-The method of using complex cepstrum of signal dereverberated will effectively remove the reverb, known as DCT method.
junhengqi
- 本程序是用MATLAB编写的孤立词的语音识别的程序，包含mel倒谱分析以及单个词的语音识别。-This procedure is used to prepare MATLAB isolated word speech recognition procedures, including mel cepstrum analysis, and single-word speech recognition.
voiceinotherdomain
- 语音信号在其他变换域中的分析技术和特性,语音信号的同态处理和倒谱分析,离散余弦变换,小波和小波包变换-Speech signal analysis technologies and features in other transform domain, homomorphic processing voice signals and cepstrum analysis, discrete cosine transform, wavelet and wavelet packet transform
mfcc2delta
- In this work, the Mel frequency Cepstrum Coefficient (MFCC) feature has been used for designing a text dependent speaker identification system. The extracted speech features (MFCC’s) of a speaker are quantized to a number of centroids using v
rastamat
- In sound processing, the mel-frequency cepstrum (MFC) is a representation 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.
mfcc
- Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively make up an MFC. They are derived a type of cepstral representation of the audio clip (a nonlinear spectrum-of-a-spectrum ). The difference between the cepstrum and the mel
signal_processing
- 对信号进行常规处理，包括如何载入数据文件，时域图，频域图，倒频谱图，如何设置图片字体，图片颜色等-The conventional signal processing, including how to load the data file, the time domain, frequency domain, cepstrum, how to set the font color pictures, pictures etc.
signals-analysis
- 常用信号分析程序，包括FFT，画包络谱，频谱，自相关、互相关，倒频谱，功率谱-Common signal analysis program, including FFT, painting envelope spectrum, spectrum, auto-correlation, cross correlation, cepstrum, power spectrum
shipinfenxi
- 语音信号处理实验，可以观看频谱、自相关、倒谱等-Speech Signal Processing, you can watch the spectrum, autocorrelation, cepstrum, etc.
mfcc
- 语音识别MFCC特征提取matlab代码。 「梅尔倒频谱系数」（Mel-scale Frequency Cepstral Coefficients，简称MFCC），是最常用到的语音特征，此参数考虑到人耳对不同频率的感受程度，因此特别适合用在语音辨识。-Speech recognition MFCC feature extraction matlab code. \ Mel cepstrum coefficient (Mel- scale Frequency Cepstral Coefficien
mfcc1
- 利用梅尔倒谱系数提取特征参数，可用于人声和昆虫声信号处理-MEL cepstrum coefficient of extraction sound characteristic parameters
Cepstral_Pitch_Detector
- 探测器基音周期的主要特点是使用二次cepstral峰值检波器的每一帧演讲,为了检测并纠正基音周期检测错误由于效果翻倍基音周期或减半等及相关现象。文件的4.5 Cepstral距探测器。pdf”提供了一个用户的指导对于这个练习。-This MATLAB exercise implements a pitch period detector based on detecting and tracking peaks in the (real) cepstrum during regions of v
lpc
- C++ implementation of Linear prediction coefficients calculation, LPS to LSF conversion and real cepstrum calculation.
signal_view
- MATLAB program for signal spectrum analysis: displays amplitude furier spectrum, linear prediction (all-pole) approximation and real cepstrum approximation.
Speech-recognition
- MFCC参数是基于人的听觉特性利用人听觉的屏蔽效应，在Mel标度频率域提取出来的倒谱特征参数。-MFCC parameters is based on human auditory characteristics using human auditory masking effect, in Mel scale frequency domain parameters of cepstrum.