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A Clustering Approach for the Detection of Acoustic/Seismic Signals of Unknown Structure

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2018年第2期56卷 1017-1029页

作者： Diamant, Roee Kipnis, Dror Zorzi, Michele Univ Haifa Dept Marine Technol IL-3498838 Haifa Israel Univ Padua Dept Informat Engn I-35122 Padua Italy

We focus on the detection of sporadic low-power acoustic/seismic signals of unknown structure and statistics, such as the detection of sound produced by marine mammals, low-power underground signals, or the discovery of events such as volcano eruptions. In these cases, since the ambient noise may be fast time varying and may include many noise transients, threshold-based detection may lead to a significant false alarm rate. Instead, we propose a detection scheme that avoids the use of a decision threshold. Our method is based on clustering the samples of the observed buffer according to a binary hidden Markov model to discriminate between "noise" and "signal" states. Our detector is a modification of the baum-welch algorithm that takes into account the expected continuity of the desired signal and obtains a robust detection using the complex but flexible general Gaussian mixture model. The result is a combination of a constrained expectation-maximization algorithm with the Viterbi algorithm. We evaluate the performance of our scheme in numerical simulations, in a seimic test, and in an ocean experiment. The results are close to the hybrid Cramer-Rao lower bound and show that, at the cost of some additional complexity, our proposed algorithm outperforms common benchmark methods in terms of detection and false alarm rates, and also achieves a better accuracy of the time of detection. To allow reproducibility of the results, we publish our code.

关键词： Acoustic detection baum-welch algorithm clustering detection in low signal-to-noise ratio (SNR) expectation-maximization ( EM) seismic detection time-of-arrival (ToA) estimation Viterbi algorithm (VA)

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A semi-hidden Fritchman Markov modeling of indoor CENELEC A narrowband power line noise based on signal level measurements

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2017年 74卷 21-30页

作者： Familua, Ayokunie D. Cheng, Ling Univ Witwatersrand Sch Elect & Informat Engn 1 Jan Smuts Ave Johannesburg South Africa

To achieve error-free communication on the narrowband power line communication (NB-PLC) channel, there is need for constant measurement campaign and modeling of burst errors resulting from noise, perturbation and disturbances on the channel. This work thus reports a signal level measurement and First Order Semi-Hidden Fritchman Markov modeling of the three major NB-PLC noise types for a typical South African residential and laboratory indoor low voltage environment. Two distinguishable disturbance scenarios: mildly disturbed and heavily disturbed were considered and baum-welch maximum likelihood estimation (MLE) algorithm is used to obtain the most probable model parameters that statistically depict the experimentally measured noise error sequences. (C) 2017 Elsevier GmbH. All rights reserved.

关键词： baum-welch algorithm Maximum likelihood estimation Narrowband Power line communications Power line noise Semi-Hidden Fritchman Markov Model

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Optimisation of HMM topology and its model parameters by genetic algorithms

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PATTERN RECOGNITION 2001年第2期34卷 509-522页

作者： Kwong, S Chau, CW Man, KF Tang, KS City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

Hidden Markov model (HMM) is currently the most popular approach to speech recognition. However, the problems of finding a good HMM model and its optimised model parameters are still of great interest to the researchers in this area. In our previous work, we have successfully applied the genetic algorithm (GA) to the HMM training process to obtain the optimised model parameters (Chau et al. Proc. ICASSP (1997) 1727) of the HMM models. In this paper, we further extend our work and propose a new training method based on GA and baum-welch algorithms to obtain an HMM model with optimised number of states in the HMM models and its model parameters. In this work, we are not only able to overcome the shortcomings of the slow convergence speed of the simple GA-HMM approach. In addition, this method also finds better number of states in the HMM topology as well as its model parameters. From our experiments with the 100 words extracted from the TIMIT corpus, our method is able to find the optimal topology in all cases. In addition, the HMMs trained by our GA HMM training have a better recognition capability than the HMMs trained by the baum-welch algorithm. In addition, 290 words are randomly selected from the TMIIT database for testing the recognition performances of both approaches, it is found that the GA-HMM approach has a recognition rate of 95.86% while the baum-welch method has a recognition rate of 93.1%. This implies that the HMMs trained by our GA-HMM method are more optimised than the HMMs trained by the baum-welch method. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： hidden Markov model genetic algorithm baum-welch algorithm speech recognition pattern recognition

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ABC algorithm based optimization of 1-D hidden Markov model for hand gesture recognition applications

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COMPUTERS IN INDUSTRY 2018年 99卷 313-323页

作者： Sagayam, K. Martin Hemanth, D. Jude Karunya Univ Dept ECE Coimbatore Tamil Nadu India

Hand gestures are extensively used to communicate based on non-verbal interaction with computers. This mode of communication is made possible by implementing machine learning algorithms for pattern recognition. A stochastic mathematical approach is used to interpret the hand gesture pattern for classification. In this work, a predominant method is used by 1-D hidden Markov model (1-D HMM) for classifying the patterns and to measure its performance. During training phase, 1-D HMM is used to predict its next state sequence of hand gestures using dynamic programming methods such as baum-welch algorithm and Viterbi algorithm. However, dynamic programming based prediction methodologies are complex. To enhance the performance of 1-D HMM model, its parameter and observation state sequence must be optimized using bio-inspired heuristic approaches. In this work, Artificial Bee Colony (ABC) algorithm is used for optimization. A hybrid 1-D HMM model with ABC optimization has been proposed which has yielded a better performance metrics like recognition rate and error rate for Cambridge hand gesture dataset.

关键词： HCI Virtual reality 1-D HMM baum-welch algorithm Viterbi algorithm ABC algorithm Cambridge hand gesture

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FFT-Based Identification of Gilbert-Elliott Data Loss Models

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021年 70卷 1-8页

作者： Palko, Andras Sujbert, Laszlo Budapest Univ Technol & Econ Dept Measurement & Informat Syst H-1521 Budapest Hungary

Nowadays, radio- or Internet-based communication is gaining popularity in various fields, e.g., in signal processing. As these are not reliable real-time communications, some of the data are lost during the transmission. There are several stochastic models allowing the analysis of this phenomenon. The spectral properties of these models result in specific distortion in the signals' spectra. As the spectrum of the signal can easily be calculated via the fast Fourier transform (FFT), FFT-based identification methods of the data loss can be developed. In this article, a new identification method is proposed for the simpler cases of the Gilbert-Elliott model class. The article summarizes the mathematical description of data loss and introduces the Gilbert-Elliott model family. The novelty of the article is the identification method that is based on the autocorrelation function of the Gilbert-Elliott model. The proposed method is compared with classical procedures based on the baum-welch algorithm and the novel ones based on global optimization. The theoretical results are supported by extensive simulations.

关键词： Autocorrelation baum-welch algorithm data loss fast Fourier transform (FFT) Gilbert-Elliott model global optimization hidden Markov model (HMM) identification

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Tractable Maximum Likelihood Estimation for Latent Structure Influence Models With Applications to EEG & ECoG Processing

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第8期45卷 10466-10477页

作者： Karimi, Sajjad Shamsollahi, Mohammad Bagher Sharif Univ Technol Sch Elect Engn BiSIPL Tehran P932 FM4 Iran

Brain signals are nonlinear and nonstationary time series, which provide information about spatiotemporal patterns of electrical activity in the brain. CHMMs are suitable tools for modeling multi-channel time-series dependent on both time and space, but state-space parameters grow exponentially with the number of channels. To cope with this limitation, we consider the influence model as the interaction of hidden Markov chains called Latent Structure Influence Models (LSIMs). LSIMs are capable of detecting nonlinearity and nonstationarity, making them well suited for multi-channel brain signals. We apply LSIMs to capture the spatial and temporal dynamics in multi-channel EEG/ECoG signals. The current manuscript extends the scope of the re-estimation algorithm from HMMs to LSIMs. We prove that the re-estimation algorithm of LSIMs will converge to stationary points corresponding to Kullback-Leibler divergence. We prove convergence by developing a new auxiliary function using the influence model and a mixture of strictly log-concave or elliptically symmetric densities. The theories that support this proof are derived from previous studies by baum, Liporace, Dempster, and Juang. We then develop a closed-form expression for re-estimation formulas using tractable marginal forward-backward parameters defined in our previous study. Simulated datasets and EEG/ECoG recordings confirm the practical convergence of the derived re-estimation formulas. We also study the use of LSIMs for modeling and classification on simulated and real EEG/ECoG datasets. Based on AIC and BIC, LSIMs perform better than HMMs and CHMMs in modeling embedded Lorenz systems and ECoG recordings. LSIMs are more reliable and better classifiers than HMMs, SVMs and CHMMs in 2-class simulated CHMMs. EEG biometric verification results indicate that the LSIM-based method improves the area under curve (AUC) values by about 6.8 and decreases the standard deviation of AUC values from 5.4 to 3.3 compared to the exis

关键词： Auxiliary function baum-welch algorithm coupled hidden Markov models EEG latent structure influence models learning problem

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Extraction of timing error parameters from readback waveforms

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IEEE TRANSACTIONS ON MAGNETICS 2006年第2期42卷 194-199页

作者： Zeng, W Kavcic, A Motwani, R Harvard Univ DEAS Cambridge MA 02138 USA Data Storage Inst Singapore 117608 Singapore

In this paper, we consider the problem of modeling the timing error process in, magnetic recording systems. We propose a discrete-valued Markov model for the timing error process, and design two methods (data-aided and nondata-aided), based on the baum-welch algorithm, to extract the model parameters from the readback waveforms. The channel model we consider is an intersymbol interference (ISI) channel with additive Gaussian noise. The continuous-time readback signal at the output of the channel is sampled at baud-rate. Simulation results show that the estimated parameters are close to the actual values and the convergence is attained in a few iterations of the baum-welch algorithm. We also demonstrate the usefulness of the accurate model extraction by comparing a fine-tuned Markov timing recovery loop to the standard Mueller and Muller detector with a tuned second-order loop filter.

关键词： baum-welch algorithm cycle-slip intersymbol interference synchronization timing recovery

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Joint turbo decoding and estimation of hidden Markov sources

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2001年第9期19卷 1671-1679页

作者： Garcia-Frias, J Villasenor, JD Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90024 USA

We describe a joint source-channel scheme for modifying a turbo decoder in order to exploit the statistical characteristics of hidden Markov sources. The basic idea is to treat the trellis describing the hidden Markov source as another constituent decoder which exchanges information with the other constituent decoder blocks. The source block uses as extrinsic information the probability of the input bits that is provided by the constituent decoder blocks. On the other hand, it produces a new estimation of such a probability which will be used as extrinsic information by the constituent turbo decoders. The proposed joint source-channel decoding technique leads to significantly improved performance relative to systems in which source statistics are not exploited and avoids the need to perform any explicit source coding prior to transmission. Lack of a priori knowledge of the source parameters does not degrade the performance of the system, since these parameters can be jointly estimated with turbo decoding.

关键词： baum-welch algorithm hidden Markov sources iterative decoding joint source-channel coding turbo codes

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Modelling noise and pulse width modulation interference in indoor visible light communication channels

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2019年 106卷 40-47页

作者： Holmes, Daniel G. Cheng, Ling Shimaponda-Nawa, Mulundumina Familua, Ayokunle D. Abu-Mahfouz, Adnan M. Univ Witwatersrand Sch Elect & Informat Engn Private Bag 3 ZA-2050 Johannesburg South Africa CSIR ZA-0184 Pretoria South Africa

Visible light communication (VLC) has the potential to supplement the growing demand for wireless connectivity. In order to achieve the full potential of VLC, channel models are required. Discrete channel models based on semi-hidden Markov models (Fritchman model) for indoor VLC using low data rate LEDs are presented. Each channel considered includes background noise and differing types of interference from fluorescent lights and pulse-width modulated (PWM) LEDs, which could be part of an indoor smart lighting system. Models were developed based on experimental error sequences from a VLC system using an on-off keying (OOK) modulation scheme. The error sequences were input into the baum-welch algorithm to determine the model parameters by expectation maximisation. Simulated error sequences generated by the models are compared to and, in most cases, perform better than simpler models with a single bit error rate. The models closely approximate the experimental errors sequences in terms of error distribution. The models performed better in channels where there is less interference. It was also found that periodic errors were introduced as a results of the PWM modulated smart lighting LEDs. These models have use for designing error control codes and simulating indoor VLC environments with different types of interference. (C) 2019 Elsevier GmbH. All rights reserved.

关键词： baum-welch algorithm Fritchman model Pulse width modulation (PWM) Semi-hidden Markov models Smart lighting Visible light communication (VLC)

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HIDDEN MARKOV-MODELS FOR SPEECH RECOGNITION

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TECHNOMETRICS 1991年第3期33卷 251-272页

作者： JUANG, BH RABINER, LR Speech Research Department ATandT Bell Laboratories Murray Hill NJ 07974 United States

The use of hidden Markov models for speech recognition has become predominant in the last several years, as evidenced by the number of published papers and talks at major speech conferences. The reasons this method has become so popular are the inherent statistical (mathematically precise) framework;the ease and availability of training algorithms for estimating the parameters of the models from finite training sets of speech data;the flexibility of the resulting recognition system in which one can easily change the size, type, or architecture of the models to suit particular words, sounds, and so forth;and the ease of implementation of the overall recognition system. In this expository article, we address the role of statistical methods in this powerful technology as applied to speech recognition and discuss a range of theoretical and practical issues that are as yet unsolved in terms of their importance and their effect on performance for different system implementations.

关键词： baum-welch algorithm INCOMPLETE DATA PROBLEM MAXIMUM A-POSTERIORI DECODING MAXIMUM LIKELIHOOD

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