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Restoration of a discrete-time signal segment by interpolation based on the left-sided and right-sided autoregressive parameters

机译:通过基于左侧和右侧自回归参数的插值恢复离散时间信号段

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This paper presents an algorithm for the interpolation of a missing signal segment on the assumption that the signal can be modeled as an autoregressive (AR) process. Unlike previous algorithms, the presented algorithm does not model the signal of the missing segment and the neighboring signal portions by a single AR-parameter vector. Instead, two separate vectors are used so that stationarity need no longer be assumed to extend beyond both sides of the missing segment. The relaxation of this stationarity assumption is essential when the duration of the missing segment is on the order of the short-time stationarity duration of the signal. The algorithm provides the optimal solution to the problem of interpolating a missing segment based on the left-sided and right-sided AR-parameter vectors. The solution is optimal in the sense of a least-squares residual. The algorithm is applied to speech and music signals and is compared with other restoration techniques.
机译:本文提出了一种在信号可以建模为自回归(AR)过程的前提下对丢失信号段进行插值的算法。与先前的算法不同,所提出的算法不通过单个AR参数向量对丢失段和相邻信号部分的信号进行建模。取而代之的是,使用了两个单独的向量,因此不再需要假定平稳性超出了缺失片段的两侧。当丢失的片段的持续时间在信号的短时平稳持续时间的数量级上时,放松平稳假设是至关重要的。该算法为基于左侧和右侧AR参数向量内插缺失段的问题提供了最佳解决方案。在最小二乘残差的意义上,该解决方案是最佳的。该算法适用于语音和音乐信号,并与其他恢复技术进行比较。

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