资源列表
pcm_coding0.vhd
- PCM编码源码-PCM coding FOSS ********************************************************
speech-signal
- 本资料中的程序可以提取语音信号的短时能量、过零率、短时谱、语谱图和线性预测系数。-The information in the program can extract short-time energy of the speech signal, the zero crossing rate, short-time spectrum, spectrogram and linear prediction coefficients.
ClusterData
- Performs hierarchical clustering of data using specified method and seraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al - Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional Catalogue Databa
tnorm
- Tnorm利用大量冒充者模型来得到均值u和方差δ，再用这两个数据带入到score =(score-u)/δ得到规整化之后的分数。这样使得结果更加合理。-Tnorm impostor model to get mean u and variance δ, then these two data into the score ' = (score-u)/δ score after regularization. This makes the results more reasonable.
time_streching_constant_hop_size
- Speech time scale modification using constant hop size
DOASimu1
- 用于声源定位的多种经典算法总结.MUSIC,ESPRIT等。-For a variety of classic sound source localization algorithm summarized. MUSIC, ESPRIT, etc..
Speech_LPC
- This GUI is used to analyze the speech signal at the selected region of 256 samples. All the calculation is based on the sampling of 8 KHz. First 3 formants of the selected block of samples are derived from the LPC-8 coefficients. -This GUI is used t
VAd
- 从语音文件中生成时变得能量直方图，类似频谱图的样子。用于分析语音-For speech analysis. Generate energy histogram frame by frame, something like spectrum.
FFT-CPP
- 可读取一个二进制文件（如去掉头部的wav文件），将得到的数值进行FFT求出离散频谱值并保存到一个文件中。-This will process the data read from a file by FFT computing, and then save the result to a new file.
LMS
- 使用matlab开发的自适应滤波器文件，现代信号处理的学生可以借鉴一个！
SpeechAddNoise_matlab
- noisegen.m：生成白噪声并按照指定信噪比对语音进行加噪； add_noisem.m：使用输入的噪音按照指定信噪比对语音进加噪； SNR_singlech.m：输入纯净语音和加噪后的语音，计算其信噪比； my_add_noise.m：调用上述函数进行加噪操作；-noisegen.m: white noise generated by the specified signal to noise ratio of the speech plus noise add_noisem.
lpc_map_2
- 该程序是线形预测LPC程序,它使用于单通道的语音增强,可取得不错的效果!-The procedure is linear prediction LPC program that used in single-channel speech enhancement can be obtained good results!