recognition signal

Blind separation of speech signals has become one of the attractive trend in the field of signal processing in recent years, owing that it has a wide application prospect in speech recognition system, computer hearing, mobile speech data communication and television and telephone conference.

因其在语音识别系统、计算机听觉、移动语音数据通信以及电视电话会议中具有广泛的应用前景，近年来日益成为信号处理领域的研究热点之一。

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Target echo signal feature extraction and material recognition of ultra-wideband ground-penetrating radar

超宽带探地雷达目标回波信号特征提取与材质识别

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The target recognition observes the following process-first resolving the wavelet of radar echo-signal, selecting the optimum wavelet bases via given training sample sets based on distance criteria to get the feature vector for target recognition, thus the targets are recognized by the effective features.

首先，对雷达回波信号进行小波分解，利用距离准则，选择最优小波包基，从被识别的信号中提取具有最大可分性的特征，得到目标识别的特征向量，由此进行目标识别。

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Method of Singularity Target Signal Multiscale Orientation and Recognition

奇异目标信号的多尺度定位识别方法

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Speech separation has been a hot topic in signal processing society recently years, which has many applications and influence in telephone conference, hearing aid, portable devices, speech recognition.

语音信号的分离近年来成为信号处理领域的一个研究热点，它在电话会议、助听器及便携设备、机器的语音识别方面有很多的应用与影响。

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According to the current method whose recognition result is inefficient and can not recognize the unknow radar signal, this paper presents a new method of the radar recognition.

针对目标所采用的辐射源识别方法对复杂体制雷达信号识别效率低或无法识别的现象，提出了一种新的辐射源识别方法。

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Speaker recognition, also known as voiceprint recognition, is an important branch of speech signal processing and is one of the most popular research fields about speech.

说话人识别又称声纹识别，是语音信号处理中的重要组成部分，是当前的研究热点之一。

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Fabric defect is singular signal, wavelet transform provides a multiresolution framework, has good time-frequency characteristics, so it has theoretical significance and application value to discuss defect detection and recognition in wavelet domin.

织物疵点是奇异信号，小波变换提供了一种多分辨率框架，具有良好的时频局部特征，因此，研究小波域的特征的疵点检测与识别具有理论意义和应用价值。

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First the speech recognition related theories and methods are introduced from the signal preprocessing, windowing and framing, endpoint detecting, feature extraction, to the training and recognition steps.

从语音信号预处理、加窗分帧、端点检测、特征参数提取、识别和训练六个环节详细阐述了语音识别的原理、处理流程及相应算法。

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The results show that speech recognition rate can be enhanced by implementing pre-processing to noisy speech signal, and the speech recognition effect of sub-band is better than full-band in a way.

通过大量实验结果表明，对带噪语音信号进行有效的语音增强预处理能提高语音识别率，而且在某种程度上子带语音识别相对于全带语音识别效果更好一些。

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