Multicomponent AM–FM Representations: An Asymptotically Exact Approach
Multicomponent AM–FM Representations: An Asymptotically Exact Approach
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DOI：
10.1109/tasl.2006.889744
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发表时间：
2007-03
期刊：
影响因子：
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通讯作者：
F. Gianfelici;G. Biagetti;P. Crippa;C. Turchetti
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文献类型：
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作者：
F. Gianfelici;G. Biagetti;P. Crippa;C. Turchetti
本文提出了一个严格的数学公式的基础上，一个多分量正弦模型，允许一个渐进的精确重建的非平稳语音信号，无论其持续时间和没有任何限制，在建模的浊音，清音，和过渡段。所提出的方法是基于希尔伯特变换的应用程序，以获得一个幅度信号，从中提取的AM分量通过滤波，使剩余的，然后可以以相同的方式迭代处理。这种技术允许一个多分量的AM-FM模型来推导，其中的组件（迭代）的数量可以任意选择。此外，这些分量的瞬时频率可以通过相位信号的分段以给定的精度计算。该方法的有效性已被证明的一些应用程序的合成信号和自然语音。几个比较表明，这种方法几乎总是具有比当前最佳实践更高的性能，并且不需要其他技术所需的复杂滤波器优化
This paper presents, on the basis of a rigorous mathematical formulation, a multicomponent sinusoidal model that allows an asymptotically exact reconstruction of nonstationary speech signals, regardless of their duration and without any limitation in the modeling of voiced, unvoiced, and transitional segments. The proposed approach is based on the application of the Hilbert transform to obtain an amplitude signal from which an AM component is extracted by filtering, so that the residue can then be iteratively processed in the same way. This technique permits a multicomponent AM-FM model to be derived in which the number of components (iterations) may be arbitrarily chosen. Additionally, the instantaneous frequencies of these components can be calculated with a given accuracy by segmentation of the phase signals. The validity of the proposed approach has been proven by some applications to both synthetic signals and natural speech. Several comparisons show how this approach almost always has a higher performance than that obtained by current best practices, and does not need the complex filter optimizations required by other techniques