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Modeling and analysis of floating point quantization errors in subband filter structures

机译:子带滤波器结构中浮点量化误差的建模与分析

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This paper is concerned with the analysis and modeling of the effects of floating point quantization of subband signals in a two channel filter bank. It represents an extension of previous work for optimal fixed-point quantizers. In the present case, the quantization noise is modeled by a multiplicative noise as compared with additive noise representation for the fixed- point case. We derive equations for the autocorrelation and power spectral density (PSD) of the reconstructed signal y(n) in terms of the analysis/synthesis filters, the PSD of the input, and the quantizer model. Formulas for the mean-square error and for compaction gain are obtained in terms of these parameters. We assume the filter bank is perfect reconstruction (PR) (but not necessarily paraunitary) in the absence of quantization and transmission errors. The autocorrelation function of the output y(n) is generally non-stationary. However, it is cyclostationary since it is stationary when n is odd, or n is even; but not both. By taking the average of the autocorrelation for n even, and for n odd, we obtain a stationary autocorrelation, and its associated PSD. This cyclostationary analysis is used to compute the quantization noise component in the output, for any PR subband structure.
机译:本文涉及两个信道滤波器组中子带信号浮点量化的效果的分析和建模。它代表了以前工作的延伸,以获得最佳的定点量化器。在当前情况下,与针对固定点外壳的附加噪声表示相比,量化噪声通过乘法噪声建模。在分析/合成滤波器,输入的PSD和量化器模型的方面,我们从重建信号Y(n)的自相关信号Y(n)的自相关和功率谱密度(PSD)的方程。根据这些参数获得平均误差和压实增益的公式。在没有量化和传输误差的情况下,我们假设过滤器组是完美的重建(但不一定是船长)。输出y(n)的自相关函数通常是非静止的。然而,睫状病性是因为奇数时静止,或者n均匀;但不是两者。通过甚至是N个奇数的自相关的平均值,我们获得了静止的自相关,以及其相关的PSD。这种循环棘轮分析用于为任何PR子带结构计算输出中的量化噪声分量。

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