搜索资源列表
voice_enhance
- 语音端点检测，以及基于短时能量的语音增强的减谱法，matlab文件。-Voice activity detection, as well as short-term energy-based speech enhancement by spectral method, matlab file.
spectral_method_program
- 用matlab编写的谱方法基本程序,相信对大家有用。-Some basic matlab programs on spetral methods
noise
- 一种基于改进谱减法的语音去噪新方法.pdf-Based on improved spectral subtraction speech denoising new method. Pdf
d9000mpi3
- 对互谱法被动测距的原理及算法各个环节进行仿真验证：加权最小二乘估计中权系数的选取；相关窗长度的选取；各频段综合方法等等。最后实现了对9000m—15000m距离的测距-Cross-spectral method for passive ranging all aspects of theory and algorithms for simulation: weighted least squares estimation of the right of the selection coeffic
PSD
- 利用最大值熵法和PSD函数法分别求信号的功率谱密度。-The use of maximum entropy method and the PSD function method for seeking signals in the respective power spectral density.
FM-HHT
- 基于HHT方法的模拟调频信号的hilbert谱分析-HHT-based method of analog FM signal hilbert spectral analysis
emd
- This program generates the printout indicated at the end of the * listing and the file SNP containing a snapshot of the magnetic field * at 30 microseconds. The calculations correspond to Figure 3. The model * is homogeneous, with the magnet
fdtd_2d_demo_v1_1
- In this paper, a two-dimensional (2-D) transverse magnetic (TM) mode finite difference time domain (FDTD) method is used to simulate the indoor radio wave propagation and model the indoor ultra wideband (UWB) channel. The modulated Gaussian p
KECA
- Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010. We introduce kernel entropy component analysis (kernel ECA) as a new method
coherencee mvdr
- —The minimum variance distortionless response (MVDR) approach is very popular in array processing. It is also employed in spectral estimation where the Fourier matrix is used in the optimization process. First, we give a general form of the M
modelbasedonspectrumprediction
- 文章展示了基于高斯混合模型的语音频谱预测方法。频谱预测可能在传包过程中预防丢包这方面起到大作用。期望最大化算法用两倍或三倍的连续语音因素来测试模型。模型被用来设计第一，儿等指令预测量。预测表用频谱分配状态来估计并和一个简单的参考模型对比。最好的预测表得到一个平均频率扭曲值是0.46dB小于参考模型-This paper presents methods for speech spectrum prediction based on Gaussian mixture models. Spec
part3
- The recent development of a meshless method by using radial basis functions will be reported in this talk. Application to both multivariate interpolation and solving partial differential equations have demonstrated the spectral convergence of the
silenceRemoval
- his a simple method for silence removal and segmentation of audio streams that contain speech. The method is based in two simple audio features (signal energy and spectral centroid). As long as the feature sequences are extracted, as thresholding app
pisa
- 在计算机上产生一组实验数据，首先产生一段零均值白噪声数据u（n）,令功率为 ，让u(n)通过一个三阶FIR： 得到y(n). .y(n)上加三个实正弦信号f1’=0.1,f2’=0.25,f3’=0.26调整 和正弦信号幅度信噪比大致为10dB,50dB,50dB. (1) 令N=256,描绘xn波形； (2)得出真实功率谱密度 . (3) 利用此实验数据Pisarenko谐波分解法估计该实验数据的正弦频率及幅度。-On the computer to generate a
pisare
- 用Pisarenko谐波分解法估计一组实验数据的正弦频率及幅度。得出真实功率谱密度。-Pisarenko harmonic decomposition method with a set of experimental data estimate the sinusoidal frequency and amplitude. The true power spectral density obtained.
HHT_power-system_power-quality_disturbances-detect
- 优秀论文及配套源码。Hilbert-Huang变换(HHT)是一种新的非平稳信号处理技术，该方法由经验模态 分解(EMD)与Hilbert谱分析两部分组成。任意的非平稳信号首先经过EMD方法处理后被分解为一系列具有不同特征尺度的数据序列，每一个序列称为一个固有模态函数(IMF)，然后对每个IMF分量进行Hilbert谱分析得到相应分量的Hilbert谱，汇总所有Hilbert谱就得到了原信号的谱图。该方法从本质上讲是对非平稳信号进行平稳化处理，将信号中真实存在的不同尺度波动或趋势逐级分解出来，最
autofam
- The M-file computes the spectral correlation function or cyclic spectral density using the FFT accummulation method.
feature
- 该文件包含了一些常用的声辐射噪声的特征提取方法，包括波形的包络特征，高阶谱特征，分形维特征，奇异指数特征等。-The document contains some common acoustic feature extraction method of noise, including waveform envelope characteristics, higher order spectral features, characteristic of fractal dimension, si
autofam.m
- FAM Method. This Method compute spectral correlation density function. This method is based on modifications of time smoothed cyclic cross periodogram.
zishiyingjiangzao
- 这里介绍了一种基于自适应滤波的噪声抵消法，采用归一化最小均方误差算法，采集实际噪声环境下各种不同信噪比的带噪语音样本进行降噪处理，实验结果表明，处理后信号的信噪比得到了较大程度的提高，大大改善了听音效果，具有很高的可懂度，且语音自然度好，没有失真；并与谱减法进行了比较，自适应噪声抵消法的降噪幅度比谱减法有一定提高，在听音效果上，用自适应噪声抵消法处理后的语音在清晰度，自然度方面优于谱减法。-Here a novel adaptive noise cancellation method using