On amplitude and frequency demodulation using energy operators

Maragos, Petros; Kaiser, J.; Quatieri, Thomas F.

doi:10.1109/78.212729

Cited by 474 publications

(262 citation statements)

References 5 publications

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“…Motivated by several nonlinear and time-varying phenomena during speech production, Maragos, Quatieri and Kaiser [2] proposed an AM-FM modulation model that represents each single speech resonance (formant) as an AM-FM signal. This model represents each resonance of a speech signal as a signal with a combined amplitude modulation (AM) and frequency modulation (FM) structure.…”

Section: Am-fm Modulation Modelmentioning

confidence: 99%

“…The nonlinear DEO, xn , first developed by Kaiser [1] and its discrete-time counterpart have found several applications in the speech processing area [2], [3]. The DEO x , which tracks the energy of a source producing an oscillation signal xt, is defined as xt = _xt 2 , xt xt; (4) where _x = dx=dt.…”

Section: Discrete-time Energy Operatormentioning

confidence: 99%

“…The i th instantaneous formant frequency signal is finst;it = fc;i + qit. In the discrete-time domain the i th ; (2) where ain is the discrete-time amplitude envelope, c;i is the carrier frequency and m is the modulation frequency. The digital instantaneous frequency of the discrete-time AM-FM signal, inst;i, is defined as inst;in = c + mqn:…”

Section: Am-fm Modulation Modelmentioning

confidence: 99%

“…This discrete-time energy operator is defined as xn = x 2 n , xn , 1xn + 1 ; n = 0; 1; ::; N , 1. In [2] it has been shown that, when the energy operator is applied to an AM-FM signal, it can approximately estimate the squared product of the amplitude and frequency signals.…”

Section: Introductionmentioning

confidence: 99%

“…The nonlinear energy operator first developed by Kaiser [1] and its discrete-time counterpart have found several applications in the speech processing area [2], [3]. This discrete-time energy operator is defined as xn = x 2 n , xn , 1xn + 1 ; n = 0; 1; ::; N , 1.…”

Section: Introductionmentioning

confidence: 99%

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Automatic speech recognition based on cepstral coefficients and a mel-based discrete energy operator

Tolba

O’Shaughnessy

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181

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In this paper, a novel feature vector based on both Mel Frequency Cepstral Coefficients (MFCCs) and a Mel-based nonlinear Discrete-time Energy Operator (MDEO) is proposed to be used as the input of an HMM-based Automatic Continuous Speech Recognition (ACSR) system. Our goal is to improve the performance of such a recognizer using the new feature vector. Experiments show that the use of the new feature vector increases the recognition rate of the ACSR system. The HTK Hidden Markov Model Toolkit was used throughout. Experiments were done on both the TIMIT and NTIMIT databases. For the TIMIT database, when the MDEO was included in the feature vector to test a multi-speaker ACSR system, we found that the error rate decreased by about 9:51. On the other hand, for NTIMIT, the MDEO deteriorates the performance of the recognizer. That is, the new feature vector is useful for clean speech but not for telephone speech.

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Section: Am-fm Modulation Modelmentioning

confidence: 99%

Section: Discrete-time Energy Operatormentioning

confidence: 99%

Section: Am-fm Modulation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic speech recognition based on cepstral coefficients and a mel-based discrete energy operator

Tolba

O’Shaughnessy

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181

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Application of multivariate signal analysis in vibration‐based condition monitoring of wind turbine gearbox

Rafiq

Rashed

Shafik

2020

Int Trans Electr Energ Syst

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Summary The accuracy of fault diagnosis and condition monitoring of mechanical systems depends on the feature extraction of a non‐stationary vibration signals acquired from multiple accelerometer sensors. Extracting fault features of such complex vibration signals is a challengeable task due to the signals masked by an intensive noise. Recently, the multivariate empirical mode decomposition (MEMD) algorithm has been proposed in order to extend empirical mode decomposition (EMD) for the multi‐channel signal and make it suitable for processing multivariate signals. It is found that, likewise, EMD, MEMD is also essentially acting as a dyadic filter bank for the multivariate input signal on each channel. However, different from EMD, MEMD better aligns the same intrinsic mode functions (IMFs) across the same frequency range from different channels, which plays an important role in real‐world applications. However, MEMD still exhibits the degree of mode mixing problem, which affects the accuracy of extracting fault features. In this article, an improved MEMD, namely NAMEMD, is proposed to extract the most meaningful multivariate IMFs by adding uncorrelated white Gaussian noise in separate channels, under certain conditions, to enhance the decomposed multivariate IMFs by minimizing mode mixing problem. After that, a new method is proposed to select the most effective multivariate IMFs related to faults. To optimize the performance of extracting vibration fault features, a proposed noise‐assisted MEMD algorithm is then combined with a competent non‐linear Teager‐Kaiser energy operator, thereby guarantees a superior fault diagnosis performance. To verify the effectiveness of the proposed method, both a synthetic analytic signal and experimental wind turbine benchmark vibration datasets are utilized and tested. The results demonstrate that the proposed approach is suited for capturing a significant fault features in wind turbine multi‐stage gearboxes, thus providing a viable multivariate signal processing tool for wind turbine gearbox condition monitoring.

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