In the financial system research, analyze the correlation relations between the multi-dimensional time series frequently, like short-term information, long-term balanced relations.
在金融系统研究中，经常分析多维时间序列之间的相关关系，如短期信息、长期均衡关系。
For that, this thesis combines short-time zero-crossing rate, the short-term energy method and frame cross-correlation method to cut the voice data. A series experiments were done afterwards.
为此本文采用了短时过零率与短时能量相结合以及相连帧互相关的方法对语音进行切割并且做了一系列的实验。
The time domain analysis is most simple and intuitionistic. The short-time energy, the short-time zero crossing rate and the short-time self-correlation are main analysis method in time domain.
时域分析方法是最简单、最直观的方法，其中我们采用短时能量、短时过零率、短时自相关函数等方法来分析语音。
The time domain analysis is most simple and intuitionistic. The short-time energy, the short-time zero crossing rate and the short-time self-correlation are main analysis method in time domain.
时域分析方法是最简单、最直观的方法，其中我们采用短时能量、短时过零率、短时自相关函数等方法来分析语音。
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