998 resultados para Functional PCA
Resumo:
提出主元分析PCA(Principal Component Analysis)用于语音检测的方法研究．用主元分析法在多维空间中建立坐标轴，将待处理信号投影到该坐标轴中，通过分析投影结果判断是否为语音信号．通过将语音和非语音分别建立子空间，来区分语音和非语音信号．该方法不同于常规的语音时域、频域处理方法，而是在多维空间中对信号进行分析·实验结果表明，该方法准确率高、简单、容易实现，而且能区分多种非语音信号．
Resumo:
本文提出了一种基于仿生模式识别和PCA/ICA的DOA估计方法.这种方法的建模过程是用在实际环境下采集的训练样本构造人工神经网络模型,对环境的适应能力较强;且这种方法采用PCA/ICA进行特征提取,使数据得到有效压缩,可以实现系统实时处理.实验结果表明:在信噪比为20dB和0dB时,该方法的正确估计率可达100%;在信噪比降为-20dB时,该方法仍有83%的可识别率.
Resumo:
The present status and future prospects of functional information materials, mainly focusing on semiconductor microstructural materials, are introduced first in this paper. Then a brief discussion how to enhance the academic level and innovation capability of research and development of functional information materials in China are made. Finally the main problems concerning the studies of materials science and technology are analyzed, and possible measures for promoting its development are proposed.
