A COMPARISON OF THE ENERGY OPERATOR AND THE HILBERT TRANSFORM APPROACH TO SIGNAL AND SPEECH DEMODULATION
A COMPARISON OF THE ENERGY OPERATOR AND THE HILBERT TRANSFORM APPROACH TO SIGNAL AND SPEECH DEMODULATION
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DOI：
10.1016/0165-1684(94)90169-4
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发表时间：
1994-05-01
影响因子：
4.4
通讯作者：
MARAGOS, P
中科院分区：
文献类型：
--
作者：
POTAMIANOS, A;MARAGOS, P
希尔伯特变换与Gabor的解析sipal提供了一种标准的线性积分方法来估计具有组合调幅（AM）和调频（FM）结构的信号的幅包络和瞬时频率。最近的一种替代方法是使用非线性微分“能量”算子来跟踪产生AM-FM信号所需的能量，并将其分离为幅度和频率分量。在本文中，我们比较了任意信号和AM-FM信号模拟的语音共振的这两种根本不同的解调方法。从估计误差的大小、计算复杂度和对瞬时信号变化的适应性等几个方面进行了比较。我们还提出了一种能量算子的改进方法，即使用简单的二项式卷积来平滑能量信号。这种平滑的能量算子与希尔伯特变换在语音元音信号的跟踪调制上进行了比较，在其共振峰周围进行了带通滤波。研究了基音周期性和带通滤波对两种解调方法的影响，并提出了在形成峰跟踪中的应用。结果有力地证明了平滑能量算子方法在语音应用中的估计误差与希尔伯特变换方法相似，但在通信应用中的估计误差较小。此外，平滑能量算子方法由于具有瞬时性，具有较小的计算复杂度和较快的自适应速度。
The Hilbert transform together with Gabor's analytic sipal provides a standard linear integral approach to estimate the amplitude envelope and instantaneous frequency of signals with a combined amplitude modulation (AM) and frequency modulation (FM) structure. A recent alternative approach uses a nonlinear differential 'energy' operator to track the energy required to generate an AM-FM signal and separate it into amplitude and frequency components. In this paper, we compare these two fundamentally different approaches for demodulation of arbitrary signals and of speech resonances modeled by AM-FM signals. The comparison is done from several viewpoints: magnitude of estimation errors, computational complexity, and adaptability to instantaneous signal changes. We also propose a refinement of the energy operator approach that uses simple binomial convolutions to smooth the energy signals. This smoothed energy operator is compared to the Hilbert transform on tracking modulations in speech vowel signals, band-pass filtered around their formants. The effects of pitch periodicity and band-pass filtering on both demodulation approaches are examined and an application to formant tracking is presented. The results provide strong evidence that the estimation errors of the smoothed energy operator approach are similar to that of the Hilbert transform approach for speech applications, but smaller for communication applications. In addition, the smoothed energy operator approach has smaller computational complexity and faster adaptation due to its instantaneous nature.