Spectrum estimation using an analytic signal representation

Picone, J.; Prezas, D.; Hartwell, W.; LoCicero, J.L.

doi:10.1016/0165-1684(88)90068-0

Cited by 20 publications

(5 citation statements)

References 8 publications

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“…The primary reason behind converting the real-valued signal into its analytic representation is to allow the use of complex-valued filters in the formant filterbank (see Section II-A below). The conversion also decreases the amount of aliasing in the signal, increasing the accuracy of the spectral estimation technique used for formant frequency estimation [24].…”

Section: The Formant Tracking Algorithmmentioning

confidence: 99%

Robust Formant Tracking for Continuous Speech With Speaker Variability

Mustafa

Bruce

2006

IEEE Trans. Audio Speech Lang. Process.

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Several algorithms have been developed for tracking formant frequency trajectories of speech signals, however most of these algorithms are either not robust in real-life noise environments or are not suitable for real-time implementation. The algorithm presented in this paper obtains formant frequency estimates from voiced segments of continuous speech by using a time-varying adaptive filterbank to track individual formant frequencies. The formant tracker incorporates an adaptive voicing detector and a gender detector for formant extraction from continuous speech, for both male and female speakers. The algorithm has a low signal delay and provides smooth and accurate estimates for the first four formant frequencies at moderate and high signal-to-noise ratios. Thorough testing of the algorithm has shown that it is robust over a wide range of signal-to-noise ratios for various types of background noises.

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Section: The Formant Tracking Algorithmmentioning

confidence: 99%

Robust Formant Tracking for Continuous Speech With Speaker Variability

Mustafa

Bruce

2006

IEEE Trans. Audio Speech Lang. Process.

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“…The very definition of the WVD demands the signal to be sampled at twice the Nyquist sampling rate, otherwise it introduces aliasing in the frequency domain as the periodicity is rr instead of 2st. This is overcome by using an analytic signal (Picone 1988) which necessitates further processing techniques to handle complex signals.…”

Section: The Wigner-ville Distribution and The Modified Magnitude Gromentioning

confidence: 99%

“…As required by the WVD, the signals are converted to analytic signals by computing the Hilbert transform of the original real signal. The Hilbert transform has been realised by convolving the signal with the impulse response of the Hilbert transformer (Picone, 1988) (weighted by a Kaiser window with a smoothing factor of 8), in time domain. freq.…”

Section: Performance Of the Improved Wvdmentioning

confidence: 99%

Power spectrum estimation of complex signals and its application to Wigner-Ville distribution: A group delay approach

Narasimhan¹,

Plotkin

Swamy

1998

Sadhana

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In this paper, a method of estimating the power spectrum of a complex signal based on the Group Delay function (GD) is proposed and also applied to the Wigner-Ville Distribution (WVD) to reduce the ripple effect due to the truncation of the autocorrelation sequence. The proposed method is realised by the GD for a complex signal and the modified GD concept. This extends the performance advantages of the modified GD applicable to a real signal, to a complex one. Further, its application to WVD, reduces the truncation/ripple effect without sacrificing any frequency resolution, as no common window function is used. Significant improvement in performance, in terms of reduction in variance without any compromise on resolution and higher noise immunity, has been found over those of the periodogram and windowed WVD.

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“…Model order of 4 [1], [13] when applied to the real signal, and model order of 2 when the "analytic" signal is used [7].…”

Section: Methodsmentioning

confidence: 99%

“…-The application of autoregressive estimators using an "analytic" signal representation produce higher frequency resolution than their real signals counterparts [7].…”

Section: Introductionmentioning

confidence: 99%

"Analytic" approximation of doppler ultrasound blood flow signals

Moura¹,

Leiria²,

Ruano³

IEEE International Workshop on Intelligent Signal Processing, 2005.

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Spectral resolution may be enhanced by complex signal representation ("analytic" signal) of the signals processed. This paper addresses the impact on statistical and computational performance of the autoregressive modified covariance spectral estimator on a clinical scenario, while using, or not, discrete "analytic" signals. The statistical performance results prove that an "analytic" signal representation should be obtained prior to the estimation process. A novel algorithm to compute the complex modified covariance matrix is used to achieve increased computational performance. This approach reduces the estimation error and the execution time by 1% and 30% respectively.

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Spectrum estimation using an analytic signal representation

Cited by 20 publications

References 8 publications

Robust Formant Tracking for Continuous Speech With Speaker Variability

Robust Formant Tracking for Continuous Speech With Speaker Variability

Power spectrum estimation of complex signals and its application to Wigner-Ville distribution: A group delay approach

"Analytic" approximation of doppler ultrasound blood flow signals

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