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.
通常的基于短时自相关的自适应线谱增强器（SABALSE）主要缺点是：输入信噪比低时，抑制高斯噪声性能差。
Traditional short-term autocorrelation-based adaptive line spectrum enhancer (SABALSE) becomes low in suppressing Gaussian noise when input signal-to-noise ratio becomes low.
对语音信号端点检测的主要方法，如基于短时能量的方法、基于HMM的方法、基于自相关相似距离的方法等进行了深入研究。
The main speech signal endpoint detection methods, such as short time energy based scheme, HMM based scheme, related alike scheme and so on are investigated deeply.
给出了一种基于短时循环自相关特性的2fsk信号快速解码算法。
This paper presents a fast demodulation algorithm of a 2fsk signal based on short time cycle autocorrelation.
在较低信噪比情况下，基于语音信号的短时相对自相关序列的短时平均幅度的端点检测能够获得较高的检测精度。
The endpoint detection based on short-time average magnitude of speech signals relative autocorrelation sequences can be detected in high accuracy under the low signal-to noise ratio.
所使用的参数是：信号的短时幅度与能量、一阶和二阶过零率、自相关函数及基音周期等。
The involved parameters are short-time amplitude and energy, lst-and2nd-order zero-crossing rate, autocorrelation function and pitch period.
所使用的参数是：信号的短时幅度与能量、一阶和二阶过零率、自相关函数及基音周期等。
The involved parameters are short-time amplitude and energy, lst-and2nd-order zero-crossing rate, autocorrelation function and pitch period.
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