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This paper presents a case study illustrating the successful utilization of the instantaneous amplitude attribute generated from the 3D seismic data in Ada Field, North Louisiana, in delineating channel-body geometry, inferring th...
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This paper presents a case study illustrating the successful utilization of the instantaneous amplitude attribute generated from the 3D seismic data in Ada Field, North Louisiana, in delineating channel-body geometry, inferring the depositional environment, and in prospecting, risk reduction, and optimization of infill well location in the Lower Cretaceous Hosston (Travis Peak in Texas) Formation, a prolific, tight-gas sandstone.
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There are many methods able to extract the instantaneous frequencies from a time series for practical applications. Gabor's method is one of the most popular and simple methods; it calculates the instantaneous phase and amplitude ...
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There are many methods able to extract the instantaneous frequencies from a time series for practical applications. Gabor's method is one of the most popular and simple methods; it calculates the instantaneous phase and amplitude of a signal by using its analytic signal. Gabor's method first uses Hilbert Transform to make the time series analytical. Then, the instantaneous frequencies are calculated by taking the derivative of unwrapped phase. The limitation of Gabor's method is that it can only be applied to time series with mono component and near zero mean. For wide band time series, Gabor's method may produce negative frequencies. In this paper, a new method, the Osculating Circle (OC) Method, is presented. The OC method is based on Gabor's method but with modified unwrapped phase calculation. This new method can provides accurate instantaneous frequency (IF) and instantaneous amplitude (IA) calculation for most of the cases.
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摘要 :
There are many methods able to extract the instantaneous frequencies from a time series for practical applications. Gabor's method is one of the most popular and simple methods; it calculates the instantaneous phase and amplitude ...
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There are many methods able to extract the instantaneous frequencies from a time series for practical applications. Gabor's method is one of the most popular and simple methods; it calculates the instantaneous phase and amplitude of a signal by using its analytic signal. Gabor's method first uses Hilbert Transform to make the time series analytical. Then, the instantaneous frequencies are calculated by taking the derivative of unwrapped phase. The limitation of Gabor's method is that it can only be applied to time series with mono component and near zero mean. For wide band time series, Gabor's method may produce negative frequencies. In this paper, a new method, the Osculating Circle (OC) Method, is presented. The OC method is based on Gabor's method but with modified unwrapped phase calculation. This new method can provides accurate instantaneous frequency (IF) and instantaneous amplitude (IA) calculation for most of the cases.
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The paper presents effective algorithms for fast measurement and estimation of the unknown changing frequency and amplitude. The method for emulating coherent sampling with changing signal parameters by the interpolation of the DF...
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The paper presents effective algorithms for fast measurement and estimation of the unknown changing frequency and amplitude. The method for emulating coherent sampling with changing signal parameters by the interpolation of the DFT to estimate the frequency and amplitude of the particular component is described. An analysis of errors of the DFT coefficients caused by the frequency variation shows that the errors have two systematic contributions: the bias part due to the window spectrum main lobe and the long-range contribution. They can be both reduced with the interpolation. The optimum for reducing the time of measurement to two periods of the fundamental component and simultaneously reducing the influences of the harmonic components as possible can be the estimation with the two-point interpolation and the Hanning window.
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Overlapped speech contains simultaneous speech of multiple speakers. The presence of overlapped speech is one of the main sources of error for speaker diarization, speech, and speaker recognition systems. Most of the existing work...
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Overlapped speech contains simultaneous speech of multiple speakers. The presence of overlapped speech is one of the main sources of error for speaker diarization, speech, and speaker recognition systems. Most of the existing works used magnitude spectrum based features for overlap detection. This work focuses on detecting overlapped speech by exploring instantaneous phase and amplitude information of speech signal. Phase characteristics are captured by the Instantaneous Frequency Spectrogram (IFSpec), while Teager-Kaiser Energy Opera-tor (TEO) based pyknograms are used for representing instantaneous amplitude. Features are learned from the IF spectrogram and TEO-based pyknogram automatically using Fully-Convolutional Neural Network (F-CNN). This work is evaluated on the SSC corpus, which has been previously used in this task. Significant performance improvement is observed when both representations are combined in an early fusion framework. The performance improvement upon combination indicates the presence of complementary information in the feature representations. Classification is performed over three different segment durations, i.e., 1 s, 500 ms, and 250 ms, to analyze the effect of segment duration over overlap detection. The effect of speaker gender present in overlapped speech is also studied in this work.
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This paper presents a time-frequency approach for fetal movement monitoring which is based on the instantaneous amplitude (IA) and instantaneous frequency (IF) of signals collected using 3axial accelerometers placed over the mater...
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This paper presents a time-frequency approach for fetal movement monitoring which is based on the instantaneous amplitude (IA) and instantaneous frequency (IF) of signals collected using 3axial accelerometers placed over the maternal abdomen. Results of a feature selection method based on receiver operating characteristic analysis shows that the mean of the IAs and deviation of the Ifs outperform other features. A support vector machine based classifier which uses these 2 features exhibits a total accuracy of 96.6% with reasonably high sensitivity and specificity.
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This paper presents a time-frequency approach for fetal movement monitoring which is based on the instantaneous amplitude (IA) and instantaneous frequency (IF) of signals collected using 3axial accelerometers placed over the mater...
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This paper presents a time-frequency approach for fetal movement monitoring which is based on the instantaneous amplitude (IA) and instantaneous frequency (IF) of signals collected using 3axial accelerometers placed over the maternal abdomen. Results of a feature selection method based on receiver operating characteristic analysis shows that the mean of the IAs and deviation of the Ifs outperform other features. A support vector machine based classifier which uses these 2 features exhibits a total accuracy of 96.6% with reasonably high sensitivity and specificity.
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摘要 :
Overlapped speech contains simultaneous speech of multiple speakers. The presence of overlapped speech is one of the main sources of error for speaker diarization, speech, and speaker recognition systems. Most of the existing work...
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Overlapped speech contains simultaneous speech of multiple speakers. The presence of overlapped speech is one of the main sources of error for speaker diarization, speech, and speaker recognition systems. Most of the existing works used magnitude spectrum based features for overlap detection. This work focuses on detecting overlapped speech by exploring instantaneous phase and amplitude information of speech signal. Phase characteristics are captured by the Instantaneous Frequency Spectrogram (IFSpec), while Teager-Kaiser Energy Operator (TEO) based pyknograms are used for representing instantaneous amplitude. Features are learned from the IF spectrogram and TEO-based pyknogram automatically using Fully-Convolutional Neural Network (F-CNN). This work is evaluated on the SSC corpus, which has been previously used in this task. Significant performance improvement is observed when both representations are combined in an early fusion framework. The performance improvement upon combination indicates the presence of complementary information in the feature representations. Classification is performed over three different segment durations, i.e., 1 s, 500 ms, and 250 ms, to analyze the effect of segment duration over overlap detection. The effect of speaker gender present in overlapped speech is also studied in this work.
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Non-stationary signal modeling is a well addressed problem in the literature. Many methods have been proposed to model non-stationary signals such as time varying linear prediction and AM-FM modeling, the later being more popular....
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Non-stationary signal modeling is a well addressed problem in the literature. Many methods have been proposed to model non-stationary signals such as time varying linear prediction and AM-FM modeling, the later being more popular. Estimation techniques to determine the AM-FM components of narrow-band signal, such as Hilbert transform, DESA1, DESA2, auditory processing approach, ZC approach, etc., are prevalent, but their robustness to noise is not clearly addressed in the literature. This is critical for most practical applications, such as in communications. We explore the robustness of different AM-FM estimators in the presence of white Gaussian noise. Also, we have proposed three new methods for IF estimation based on non-uniform samples of the signal and multi-resolution analysis. Experimental results show that ZC based methods give better results than the popular methods such as DESA in clean condition as well as noisy condition.
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摘要 :
Non-stationary signal modeling is a well addressed problem in the literature. Many methods have been proposed to model non-stationary signals such as time varying linear prediction and AM-FM modeling, the later being more popular....
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Non-stationary signal modeling is a well addressed problem in the literature. Many methods have been proposed to model non-stationary signals such as time varying linear prediction and AM-FM modeling, the later being more popular. Estimation techniques to determine the AM-FM components of narrow-band signal, such as Hilbert transform, DESA1, DESA2, auditory processing approach, ZC approach, etc., are prevalent, but their robustness to noise is not clearly addressed in the literature. This is critical for most practical applications, such as in communications. We explore the robustness of different AM-FM estimators in the presence of white Gaussian noise. Also, we have proposed three new methods for IF estimation based on non-uniform samples of the signal and multi-resolution analysis. Experimental results show that ZC based methods give better results than the popular methods such as DESA in clean condition as well as noisy condition.
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