Rayleigh modeling of teager energy operated perceptual wavelet packet coefficients for enhancing noisy speech

Islam, Tauhidul; Shahnaz, Celia; Zhu, Wei-Ping; Ahmad, M. Omair

doi:10.1016/j.specom.2016.11.002

Cited by 10 publications

(24 citation statements)

References 35 publications

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“…TKEO is an energy-tracking operator that is used to estimate the instantaneous energy of a signal in the time domain. It was first proposed by Teager [28] for application in speech analysis [29] and derived by Kaiser [30], and it is efficiently and effectively estimated from three adjacent samples in real-time. The TKEO is defined in both continuous and discretized forms [31] as expressed in Equations (5) and (6), respectively.…”

Section: Teager-kaiser Energy Operatormentioning

confidence: 99%

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Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors

Wang

Zhen

et al. 2019

Energies

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Broken rotor bar (BRB) faults are one of the most common faults in induction motors (IM). One or more broken bars can reduce the performance and efficiency of the IM and hence waste the electrical power and decrease the reliability of the whole mechanical system. This paper proposes an effective fault diagnosis method using the Teager–Kaiser energy operator (TKEO) for BRB faults detection based on the motor current signal analysis (MCSA). The TKEO is investigated and applied to remove the main supply component of the motor current for accurate fault feature extraction, especially for an IM operating at low load with low slip. Through sensing the estimation of the instantaneous amplitude (IA) and instantaneous frequency (IF) of the motor current signal using TKEO, the fault characteristic frequencies can be enhanced and extracted for the accurate detection of BRB fault severities under different operating conditions. The proposed method has been validated by simulation and experimental studies that tested the IMs with different BRB fault severities to consider the effectiveness of the proposed method. The obtained results are compared with those obtained using the conventional envelope analysis methods and showed that the proposed method provides more accurate fault diagnosis results and can distinguish the BRB fault types and severities effectively, especially for operating conditions with low loads.

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Section: Teager-kaiser Energy Operatormentioning

confidence: 99%

“…In their research, the TKEO method was used to improve the signal-to-noise ratio (SNR) and extract the faults severities effectively. As a method for determination of the amplitude and frequency of a signal, TKEO was also developed to analyze the speech and evaluated to be effective in car and multi-talker babble noise environments [28].…”

Section: Introductionmentioning

confidence: 99%

Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors

Wang

Zhen

et al. 2019

Energies

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“…an appropriate similarity measure should be used to find a more accurate threshold value [21,31]. Due to the nonstationary structure of speech, the correct pdf estimate of speech or its energy is difficult.…”

Section: Generalized Gamma Statistical Modelmentioning

confidence: 99%

“…The threshold value for each mode must be adaptable in order to increase the speech quality and ensure intelligibility. Considering the probability distribution function (pdf) of the signal energy E k (t) , an appropriate similarity measure should be used to find a more accurate threshold value [21,31]. Due to the nonstationary structure of speech, the correct pdf estimate of speech or its energy is difficult.…”

Section: Generalized Gamma Statistical Modelmentioning

confidence: 99%

Speech enhancement using adaptive thresholding based on gamma distribution of Teager energy operated intrinsic mode functions

Arslan¹,

Engin²

2019

Turk J Elec Eng & Comp Sci

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This paper introduces a new speech enhancement algorithm based on the adaptive threshold of intrinsic mode functions (IMFs) of noisy signal frames extracted by empirical mode decomposition. Adaptive threshold values are estimated by using the gamma statistical model of Teager energy operated IMFs of noisy speech and estimated noise based on symmetric Kullback–Leibler divergence. The enhanced speech signal is obtained by a semisoft thresholding function, which is utilized by threshold IMF coefficients of noisy speech. The method is tested on the NOIZEUS speech database and the proposed method is compared with wavelet-shrinkage and EMD-shrinkage methods in terms of segmental SNR improvement (SegSNR), weighted spectral slope (WSS), and perceptual evaluation of speech quality (PESQ). Experimental results show that the proposed method provides a higher SegSNR improvement in dB, lower WSS distance, and higher PESQ scores than wavelet-shrinkage and EMD-shrinkage methods. The proposed method shows better performance than traditional threshold-based speech enhancement approaches from high to low SNR levels.

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“…To enable more distinctive separation between speech and noise wavelet coefficients in each subband, various techniques have been proposed. Instead of using the octave-band WT, the wavelet packet transform (WPT) was employed for fine-resolution uniform subband decomposition in [31]- [34], and the perceptual wavelet packet (PWP) for non-uniform subbands based on models of human auditory speech perception in [35]- [38]. To provide improved extraction of speech from noise, the Teager energy (TE) operator was also utilized in [31], [37], and [38].…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Adaptive Wavelet Thresholding for Speech Enhancement Using Generalized Gaussian Priors and Frame-Wise Context Modeling

2020

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This work aims at developing an adaptive wavelet thresholding algorithm for speech enhancement with significant performance improvement over other wavelet-based counterparts. This is accomplished through the formulation of the optimum threshold for noise reduction, based on the generalized Gaussian priors to fully characterize the statistics of speech and noise wavelet coefficients. In addition, through the frame-wise context modeling which enables tracking of the statistical characteristics of each individual coefficient on the frame-wise basis, the optimum threshold is accurate and adaptive at both the coefficient level and frame level. The frame-wise context model is formulated by virtue of the context subspace projection of the wavelet coefficients, with the context index employed as the invariant correspondence between successive frame parameters, thereby enabling the frame-wise tracking at the coefficient level. Simulation results show significant improvement over the wavelet-based speech enhancement algorithms in terms of the segmental signal-to-noise ratio improvement by as much as 226%, the perceptual evaluation of speech quality by 36%, the short-time objective intelligibility by 17.8% and the cepstral distance by 33.3%. When benchmarked with the well-established short-time-Fouriertransform-based counterparts, the proposed wavelet thresholding algorithm offers favorable and more robust performances, particularly under non-stationary noise conditions, with no adverse musical noise effect.INDEX TERMS context modeling, speech enhancement, wavelet thresholding.

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Rayleigh modeling of teager energy operated perceptual wavelet packet coefficients for enhancing noisy speech

Cited by 10 publications

References 35 publications

Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors

Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors

Speech enhancement using adaptive thresholding based on gamma distribution of Teager energy operated intrinsic mode functions

Performance Improvement of Adaptive Wavelet Thresholding for Speech Enhancement Using Generalized Gaussian Priors and Frame-Wise Context Modeling

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