-
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. Spectrum prediction may be useful in a packet transmission system where the sensitivity to packet losses is a major problem.
Models of speech are trained by the Expectation Maximization algorithm using pairs, triples etc. of consecutive cepstral vectors.
The models are used to design first, second etc. order predictors.
The prediction schemes are evaluated using the spectral distortion criterion and compared to a simple reference method. The
best prediction scheme obtains an average spectral distortion that
is 0.46 dB less than for the reference method. )
- 2010-07-06 16:48:31下载
- 积分：1
-
Radar-system-simulation-MATLAB-Code
雷达系统仿真Matlab源码，全部通过测试，对雷达的各个模块有仿真，适合科研和专业人士学习和研究。(Radar system simulation Matlab source, all passed the tests, each module of the radar simulation for research and professional learning and research.)
- 2011-10-30 08:49:21下载
- 积分：1
-
locthichnghiLMS
materials on LMS adaptive filter
- 2011-12-26 19:31:17下载
- 积分：1
-
神经模糊预测控制及其matlab实现
神经模糊预测控制及其matlab实现，包含书籍中各类案例代码(Neuro-Fuzzy Predictive Control and Its Matlab Implementation)
- 2018-12-09 10:32:54下载
- 积分：1
-
IPCA
说明： 主成分分析（PCA）的增量实施。
该算法在线更新每个变换系数矩阵新样本，而无需将所有样本保留在内存中。
在某种意义上，该算法在形式上等效于通常的批处理版本给定一个样本，将变换系数设置在末尾过程是相同的。 实时讨论了应用PCA的意义。(Incremental implementation of the principal component analysis (PCA).
The algorithm updates the transformation coefficients matrix on-line for each new sample, without the need to keep all the samples in memory.
The algorithm is formally equivalent to the usual batch version, in the sense that given a sample set the transformation coefficients at the end of the process are the same. The implications of applying the PCA in real time are discussed with the help of data analysis examples (a sample set is uploaded together with the examples))
- 2020-01-12 22:34:24下载
- 积分：1
-
Stochastic
this file implement stochastic method in pattern recognition
- 2013-11-02 23:39:02下载
- 积分：1
-
WindyGridWorldQLearning
Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian
domains. It amounts to an incremental method for dynamic programming which imposes limited computational
demands. It works by successively improving its evaluations of the quality of particular actions at particular states.
This paper presents and proves in detail a convergence theorem for Q,-learning based on that outlined in Watkins
(1989). We show that Q-learning converges to the optimum action-values with probability 1 so long as all actions
are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions
to the cases of non-discounted, but absorbing, Markov environments, and where many Q values can be changed
each iteration, rather than just one.
- 2013-04-19 14:23:35下载
- 积分：1
-
paper5
Wireless Information and Power Transfer in Multiuser OFDM Systems
- 2015-02-23 16:41:12下载
- 积分：1
-
online-movie
online library is a project for b.tech students
- 2014-06-26 16:08:22下载
- 积分：1
-
svmdemo
svpwm is being implemented using matlab simulation blocks
- 2010-09-09 14:15:53下载
- 积分：1
|
联系客服（QQ：3324461878）
