And based on the experimental results of multi-dimensional data clustering, anomaly detection matrix is determined through identifying the training sample set and the machine self-learning.

然后根据对多维数据聚类的实验分析结果，通过对样本集的训练进行标识和机器自学习过程来判别异常检测矩阵。

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VAD algorithm based on RASTA-filter multi-dimensional speech feature and Support Vector Machine is presented. It applies to the speech detection under the low SNR conditions.

提出了一种基于RASTA滤波技术的多维语音特征和支持向量机分类的VAD算法，适用于低信噪比情况下的话音检测。

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