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12th IEEE Digital Signal Processing Workshop/4th IEEE Signal Processing Education Workshop

作者： Bilas Pachori, Ram Sircar, Pradip Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)1424405343

In this paper, a new technique based on the Fourier-Bessel (FB) expansion is presented for separating multiple formants of a speech signal. The discrete energy separation algorithm (DESA) is applied to an isolated speech formant to extract the instantaneous frequency (IF) and the time-varying amplitude envelope (AE) of the formant. It is demonstrated that the proposed technique which is called the FB-DESA technique is a powerful tool for speech formant analysis. The technique is based on simple principle and it is easy to implement.

关键词： AM-FM model discrete energy separation algorithm Fourier-Bessel expansion speech analysis

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EEG Signal Analysis based on EMD and discrete energy separation algorithm

EEG Signal Analysis based on EMD and Discrete Energy Separat...

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作者： Potočňák, Tomáš Brno University of Technology

This paper deals with spectral analysis of nocturnal EEG signal from apnoea/hypopnea patients. Main goal is to employ methods independent to Fourier Transform, because of nonstationary character of signal, to better description of frequency changes. For this purpose, analysis based on Empirical Mode Decomposition and discrete energy separation algorithm was tested. This method is similar to commonly used Hilbert Huang Transform, but can provide higher time and frequency resolution due to algorithms based on Teager-Keiser energy Operator, which can work with very short time window.

关键词： nocturnal EEG Empirical Mode Decomposition Teager-Keiser energy Operator discrete energy separation algorithm

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A new fast method for improvement of power transformer differential protection based on discrete energy separation algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & energy SYSTEMS 2022年 136卷 107759-107759页

作者： Moosavi, Seyed Masood Lal Damchi, Yaser Assili, Mohsen Shahrood Univ Technol Fac Elect Engn Shahrood Iran

Differential protection is one of the most widely used protections in power transformers. The challenges associated with this protection are distinguishing the inrush current, overexcitation, and external fault conditions from internal and winding faults. So far, various methods have been proposed to prevent differential protection from mal-operation, which are not necessarily simple to implement and do not guarantee the proper performance in all scenarios. This paper proposes a new method based on the discrete energy separation algorithm to improve differential protection. The proposed method consists of two sections. The first section detects internal disturbances from external faults, and the other distinguishes the inrush current and overexcitation from internal and winding faults. One of the advantages of the proposed method is the parallel employment of these two sections in the logic of the method, which reduces the detection time. Moreover, other advantages such as the fast detection speed (1.5 ms), simple implementation, and low computational burden make it possible to implement the method in real-time. The simulation results show the appropriate efficiency of the proposed method in detecting all disturbances even in noisy conditions and saturation of current transformers.

关键词： Differential protection discrete energy separation algorithm Inrush current Overexcitation Power transformer

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Online structural identification by Teager energy Operator and blind source separation

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SMART STRUCTURES AND SYSTEMS 2020年第2期26卷 135-146页

作者： Ghasemi, Vida Amini, Fereidoun Iran Univ Sci & Technol Dept Civil Engn Tehran Iran

This paper deals with an application of adaptive blind source separation (BSS) method, equivariant adaptive separation via independence (EASI), and Teager energy Operator (TEO) for online identification of structural modal parameters. The aim of adaptive BSS methods is recovering a set of independent sources from their unknown linear mixtures in each step when a new sample is received. In the proposed approach, firstly, the EASI method is used to decompose structural responses into independent sources at each instance. Secondly, the TEO based demodulation method with discrete energy separation algorithm (DESA-1) is applied to each independent source, and the instantaneous frequencies and damping ratios are extracted. The DESA-1 method can provide the fast time response and has high resolution so it is suitable for online problems. This paper also compares the performance of DESA-1 algorithm with Hilbert transform (HT) method. Compared to HT method, the DESA-1 method requires smaller amounts of samples to estimate and has a smaller computational complexity and faster adaption due to instantaneous characteristic. Furthermore, due to high resolution of the DESA-1 algorithm, it is very sensitive to noise and outliers. The effectiveness of the proposed approach has been validated using synthetic examples and a benchmark structure.

关键词： adaptive blind source separation discrete energy separation algorithm equivariant adaptive separation via independence algorithm online structural identification Teager-energy Operator

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A Novel AFM Signal Model for Parametric Representation of Speech Phonemes

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2019年第9期38卷 4079-4095页

作者： Bansal, Mohan Sircar, Pradip Indian Inst Technol Kanpur Dept Elect Engn Kanpur 208016 UP India

A new multicomponent multitone amplitude and frequency-modulated signal model for parametric modelling of speech phoneme (voiced and unvoiced) is presented in this paper. As the speech signal is a multicomponent non-stationary signal, the Fourier-Bessel expansion is used to separate all individual components from the multicomponent speech signal. The parameter estimation is done by analysing the amplitude envelope (AE) and instantaneous frequency (IF) of the signal component separately. The AE and IF functions for separated components are extracted by using the discrete energy separation algorithm. The amplitude-modulated signal parameters and the amplitude of the signal are estimated by analysing the AE function, whereas the frequency-modulated signal parameters and the carrier frequency of the signal are estimated by analysing the IF function. This technique is found to be quite efficient for accurate parameter estimation of the speech phoneme. As an illustration of model-based speech processing, the proposed model is used for various speech signal processing applications.

关键词： Amplitude and frequency modulation discrete energy separation algorithm Fourier-Bessel expansion Parametric model Speech analysis synthesis

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A Nonparametric Approach for Multicomponent AM-FM Signal Analysis

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2020年第12期39卷 6316-6357页

作者： Upadhyay, Abhay Sharma, Manish Pachori, Ram Bilas Sharma, Rajeev Bundelkhand Univ Inst Engn & Technol Dept Elect & Commun Engn Jhansi 284128 Uttar Pradesh India Inst Infrastruct Technol Res & Management Sch Elect Engn Ahmadabad 380026 Gujarat India Indian Inst Technol Indore Discipline Elect Engn Indore 453552 India Birla Inst Technol Mesra Dept Elect & Commun Engn Ranchi 815215 Bihar India

In this paper, a novel method is presented to analyze the amplitude modulated and frequency modulated (AM-FM) multicomponent signals using a combination of the variational mode decomposition (VMD) and the discrete energy separation algorithm (DESA). In the presented method, firstly, a multicomponent signal is decomposed using VIVID method applied in an iterative way. In order to separate the monocomponent signals from multicomponent signal, a suitable convergence criterion is developed based on the values of estimated center frequencies ((CF) over bar) and standard deviations (sigma(CF)) of the decomposed components. Further, the estimation of amplitude envelope and the instantaneous frequency functions of monocomponent AM-FM signals has been carried out by employing DESA. Moreover, the proposed method is also applied on the synthetic AM-FM signal and speech signals to evaluate its performance. Furthermore, its performance is also compared with the Fourier-Bessel series expansion-based DESA, empirical wavelet transform-based DESA, and iterative eigenvalue decomposition-based DESA methods. The performance of the proposed method is compared with the other methods in terms of mean square error between actual and estimated amplitude envelopes (MSEAE), mean square error between actual and estimated instantaneous frequencies (MSEIF) for synthetic signal. The COSH distance measure is used as a performance measure for speech signals. It is found that the proposed method gives better results in terms of performance measures in several cases.

关键词： Variational mode decomposition method discrete energy separation algorithm Amplitude modulated and frequency modulated signal model Non-stationary signal analysis

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Low bit-rate speech coding based on multicomponent AFM signal model

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INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2018年第4期21卷 783-795页

作者： Bansal, Mohan Sircar, Pradip Indian Inst Technol Kanpur Dept Elect Engn Kanpur 208016 Uttar Pradesh India

In this paper, we propose a novel multicomponent amplitude and frequency modulated (AFM) signal model for parametric representation of speech phonemes. An efficient technique is developed for parameter estimation of the proposed model. The Fourier-Bessel series expansion is used to separate a multicomponent speech signal into a set of individual components. The discrete energy separation algorithm is used to extract the amplitude envelope (AE) and the instantaneous frequency (IF) of each component of the speech signal. Then, the parameter estimation of the proposed AFM signal model is carried out by analysing the AE and IF parts of the signal component. The developed model is found to be suitable for representation of an entire speech phoneme (voiced or unvoiced) irrespective of its time duration, and the model is shown to be applicable for low bit-rate speech coding. The symmetric Itakura-Saito and the root-mean-square log-spectral distance measures are used for comparison of the original and reconstructed speech signals.

关键词： Parametric model Non-stationary signal analysis Multi-tone amplitude and frequency modulation Fourier-Bessel expansion discrete energy separation algorithm Speech coding

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