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检索条件"主题词=Expectation-Maximization algorithm"

共 628 条记录，以下是461-470 订阅

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Gender-dependent and speaker-dependent speech enhancement

Gender-dependent and speaker-dependent speech enhancement

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Potamitis, I Fakotakis, N Kokkinakis, G Univ Patras Dept Elect & Comp Engn Wire Commun Lab GR-26110 Patras Greece

ISBN: (纸本)0780374029

Our work introduces a speech enhancement technique that can explicitly incorporate prior information about the gender or speaker time-frequency characteristics in its formalism. We approximate the multimodal, clean speech linear spectrum magnitude with a mixture of Gaussians pdfs using the expectation-maximization algorithm (EM). Subsequently, we apply the Bayesian inference framework to the degraded spectral coefficients and by employing Minimum Mean Square Error Estimation (MMSE) we derive a closed form solution for the spectral magnitude estimation task adapted to the spectral characteristics and noise variance of each band. We suggest that 2-3 minutes of phonetically balanced non-degraded gender or speaker dependent speech is adequate to tune our algorithm. We demonstrate the benefit of using an enhancement technique tailored to a specific gender or speaker and propose its use in cases where message ambiguity is of critical importance. We evaluate of our algorithm using Lynx helicopter and White Gaussian noise on the task of improving the quality of speech and in combination with a speech coder and demonstrate its robustness at very low SNRs. Implementation code is available at: http://***/potamitis/***.

关键词： speech enhancement least mean squares methods white noise Gaussian noise acoustic noise iterative methods Bayes methods gender-dependent speech enhancement speaker-dependent speech enhancement prior information speaker time-frequency characteristics multimodal clean speech linear spectrum magnitude Gaussian pdfs expectation-maximization algorithm Bayesian inference framework degraded spectral coefficients minimum mean square error estimation MMSE closed form solution spectral magnitude estimation task phonetically balanced nondegraded speech Lynx helicopter noise white Gaussian noise quality of speech speech coder

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Model-Based Estimation of Mental Workload in Drivers Using Pupil Size Measurements

Model-Based Estimation of Mental Workload in Drivers Using P...

引用

IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

作者： Pillai, Prarthana Balasingam, Balakumar Biondi, Francesco N. Univ Windsor Dept Elect & Comp Engn 401 Sunset Ave Windsor ON N9G 3P4 Canada Univ Windsor Fac Human Kinet 401 Sunset Ave Windsor ON N9G 3P4 Canada

ISBN: (纸本)9781665476331

Passenger vehicles are increasingly adopting the use of automated driving systems (ADS) to help ease the workload of drivers and to improve road safety. These systems require human drivers to constantly maintain supervisory control of the ADS. For safe adoption and ADS, the attention or alertness of the driver needs to be continuously monitored. Past studies have demonstrated pupil dilation as an effective measure of cognitive load. However, the raw pupil data recorded using eye trackers are noisy which may result in poor classification of the cognitive load levels of the driver. In this paper, an approach to reduce the noise raw pupil size data obtained from eye trackers used by ADS is proposed. The proposed approach uses a Kalman filter to filter out high-frequency noise that arises due to sudden changes in ambient light, head/body movement, and measurement noise. Data collected from 16 participants were used to demonstrate the performance of the model-based pupil-size filtering approach presented in this paper. Results show an objective improvement in the potential to distinguish changes in pupil size due to various levels of cognitive workload experienced by participants.

关键词： Cognitive load detection expectation-maximization algorithm Eye-tracking Human-computer interface Kalman filter Physiological signals Pupil size and State-space model

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Research on Russian Cultural Transliteration algorithm Based on Hidden Markov Model 24

Research on Russian Cultural Transliteration Algorithm Based...

引用

International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Wang, Hongling Liu, Chunfu Heilongjiang Univ Technol Sch Int Educ Jixi Peoples R China

ISBN: (纸本)9798400718144

Transliteration of cultural terminology poses unique challenges in natural language processing, particularly for the Russian language with its rich cultural lexicon. This research addresses these challenges by developing a transliteration algorithm tailored to Russian cultural terms using a Hidden Markov Model (HMM). The proposed approach enhances the Speech Assessment Methods Phonetic Alphabet (SAMPA)-based phoneme set to account for the phonetic intricacies of cultural vocabulary, where stress and vowel reduction patterns play crucial roles. A specialized Russian cultural pronunciation dictionary containing over 10,000 terms, encompassing historical, literary, and folk terminology is involved. The proposed algorithm employs a data-driven methodology, starting with a "many-to-many" alignment using the expectation-maximization algorithm to accommodate the diverse phonetic representations of cultural terms. The alignment is further refined through a joint N-gram model and encapsulated into a robust HMM, which effectively models the probabilistic nature of phonetic variations. The decoding phase of the HMM accurately predicts the pronunciation of complex cultural terms. The algorithm's efficacy was evaluated through meticulous cross-validation, yielding an average word-form accuracy of 71.2% and a phoneme accuracy of 89.7%, demonstrating its potential in enhancing the accessibility and preservation of Russian cultural heritage in digital formats. CCS CONCEPTS similar to Computing methodologies similar to Machine learning similar to Machine learning algorithms similar to Dynamic programming for Markov decision processes

关键词： Cultural Transliteration Russian Hidden Markov Model SAMPA expectation-maximization algorithm

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The Optimal Hyperparameter for Bayesian Clustering and its Application to the Evaluation of Clustering Results 7

The Optimal Hyperparameter for Bayesian Clustering and its A...

引用

Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS)

作者： Yamazaki, Keisuke Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku G5-194259 Nagatsuta Yokohama Kanagawa Japan

ISBN: (纸本)9781479959556

In a probabilistic approach to cluster analysis, parametric models, such as a mixture of Gaussian distributions, are often used. Since the parameter is unknown, it is necessary to estimate both the parameter and the labels of the clusters. Recently, the statistical properties of Bayesian clustering have been studied. The theoretical accuracy has been analyzed, and it has been found to be better than the maximum-likelihood method, which is based on the expectation-maximization algorithm. However, the effect of a prior distribution on the clustering result remains unknown. In the present paper, we theoretically and experimentally investigate the behavior of the optimal hyperparameter, which is the parameter of the prior distribution, and we propose an evaluation method for the clustering result, based on the prior optimization.

关键词： Bayes methods Gaussian distribution expectation-maximisation algorithm optimisation pattern clustering Bayesian clustering cluster analysis expectation-maximization algorithm maximum-likelihood method optimal hyperparameter parameter estimation parametric model prior distribution prior optimization probabilistic approach statistical property Accuracy Maximum likelihood estimation Optimization Probabilistic logic Shape

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Asset Life Prediction Using Multiple Degradation Indicators and Lifetime Data: a Gamma-based State Space Model Approach

Asset Life Prediction Using Multiple Degradation Indicators ...

引用

8th International Conference on Reliability, Maintainability and Safety (ICRMS 2009)

作者： Zhou, Yifan Ma, Lin Mathew, J. Kim, H. Wolff, Rodney Queensland Univ Technol Sch Engn Syst CRC Integrated Engn Asset Management Brisbane Qld 4001 Australia Queensland Univ Technol Sch Math Sci Brisbane Qld 4001 Australia

ISBN: (纸本)9781424449033

This paper proposes a Gamma-based state space model to predict engineering asset life when multiple degradation indicators are involved and the failure threshold on these indicators are uncertain. Monte Carlo-based parameter estimation and model inference algorithms are developed to deal with the proposed Gamma-based state space model. A case study using real data from industry is conducted to compare the performance of the proposed model with the commonly used proportional hazard model (PHM). The result shows that the Gamma-based state space model is more appropriate to deal with the situation when the failure data is insufficient.

关键词： expectation-maximization algorithm Gamma process Proportional hazard model State space model

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Learning Finite Mixture of Ising Graphical Models

Learning Finite Mixture of Ising Graphical Models

引用

作者： Gu, Chong Western Michigan University

学位级别：Ph.D., Doctor of Philosophy

The Ising model is valuable in examining complex interactions within a system, but its estimation is challenging. In this work, we proposed penalized likelihood procedures to infer conditional dependence structure when observed data come from heterogeneous resources in high-dimensional setting. The proposed method can be efficiently implemented by taking advantage of coordinate-ascent, minorization–maximization principles and EM algorithm. A BIC-type criterion will be utilized for the selection of the tuning parameter in the penalized likelihood approaches. The effectiveness of the proposed method is supported by simulation studies and a real-world example.

关键词： expectation-maximization algorithm Heterogeneous conditional dependencies Mixture of Ising graphical models Complex interactions Real-world example

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AN EM-algorithm APPROACH FOR THE DESIGN OF ORTHONORMAL BASES ADAPTED TO SPARSE REPRESENTATIONS

AN EM-ALGORITHM APPROACH FOR THE DESIGN OF ORTHONORMAL BASES...

引用

2010 IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Dremeau, A. Herzet, C. INRIA Ctr Rennes Bretagne Atlantique F-35000 Rennes France

ISBN: (纸本)9781424442966

In this paper, we consider the problem of dictionary learning for sparse representations. Several algorithms dealing with this problem can be found in the literature. One of them, introduced by Sezer et al. in [1] optimizes a dictionary made up of the union of orthonormal bases. In this paper, we propose a probabilistic interpretation of Sezer's algorithm and suggest a novel optimization procedure based on the EM algorithm. Comparisons of the performance in terms of missed detection rate show a clear superiority of the proposed approach.

关键词： Sparse representations dictionary learning expectation-maximization algorithm

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Combining Machine Learning and Information Retrieval Techniques for Software Clustering

Combining Machine Learning and Information Retrieval Techniq...

引用

1st International Workshop on Eternal Systems (EternalS 2011)

作者： Corazza, Anna Di Martino, Sergio Maggio, Valerio Scanniello, Giuseppe Univ Naples Federico II Naples Italy Univ Basilicata Potenza Italy

ISBN: (纸本)9783642280320

In the field of Software Maintenance the definition of effective approaches to partition a software system into meaningful subsystems is a longstanding and relevant research topic. These techniques are very important as they can significantly support a Maintainer in his/her tasks by grouping related entities of a large system into smaller and easier to comprehend subsystems. In this paper we investigate the effectiveness of combining information retrieval and machine learning techniques in order to exploit the lexical information provided by programmers for software clustering. In particular, differently from any related work, we employ indexing techniques to explore the contribution of the combined use of six different dictionaries, corresponding to the six parts of the source code where programmers introduce lexical information, namely: class, attribute, method and parameter names, comments, and source code statements. Moreover their relevance is estimated on the basis of the project characteristics, by applying a machine learning approach based on a probabilistic model and on the expectation-maximization algorithm. To group source files accordingly, two clustering algorithms have been compared, i.e. the K-Medoids and the Group Average Agglomerative Clustering, and the investigation has been conducted on a dataset of 9 open source Java software systems.

关键词： expectation-maximization algorithm Information Retrieval Probabilistic Model Remodularization Software Clustering Software Evolution

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Incremental Learning of Multivariate Gaussian Mixture Models

引用

Joint Conference of 20th Brazilian Symposium on Artificial Intelligence (SBIA) /SBRN/JRI

作者： Engel, Paulo Martins Heinen, Milton Roberto UFRGS Informat Inst BR-91501970 Porto Alegre RS Brazil

ISBN: (纸本)9783642161377

This paper presents a new algorithm for unsupervised incremental learning based on a Bayesian framework. The algorithm, called IGMM (for Incremental Gaussian Mixture Model), creates and continually adjusts a Gaussian Mixture Model consistent to all sequentially presented data. IGMM is particularly useful for on-line incremental clustering of data streams, as encountered in the domain of mobile robotics and animats. It creates an incremental knowledge model of the domain consisting of primitive concepts involving all observed variables. We present some preliminary results obtained using synthetic data and also consider practical issues as convergence properties discuss future developments.

关键词： Incremental Learning Unsupervised Learning Bayesian Methods expectation-maximization algorithm Finite Mixtures Clustering

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Kalman Filtering to Track Changes in Pupil Size for Automated Driving Systems 94

Kalman Filtering to Track Changes in Pupil Size for Automate...

引用

94th IEEE Vehicular Technology Conference (VTC-Fall)

作者： Pillai, Prarthana Balasingam, Balakumar Jaekel, Arunita Biondi, Francesco Univ Windsor Dept Elect & Comp Engn Windsor ON Canada Univ Windsor Sch Comp Sci Windsor ON Canada Univ Windsor Dept Kinesiol Windsor ON Canada

ISBN: (纸本)9781665413688

Automation has become indispensable in all walks of everyday life. In driving environments, Automated Driving Systems (ADS) aid the driver by reducing the required workload and by improving road safety. These systems require human drivers to remain vigilant and maintain supervisory control over ADS. Therefore, the cognitive attention of the drivers must be estimated accurately for the safe adoption of ADS. Because of the non-invasive recording setup used in low-cost infrared eye-trackers, pupil size measurements are increasingly becoming applicable in the estimation of cognitive load. However, pupil size measurements are highly noisy, resulting in the poor classification of cognitive load levels. In this paper, we propose a methodology for improved classification of changes in cognitive load through the introduction of a state-space model-based approach to filter the pupil size data. The proposed approach was demonstrated on data collected from 16 participants while they performed driving task and several secondary tasks that are designed to emulate three different levels of driving distraction.

关键词： Cognitive load detection state-space model Kalman filter expectation-maximization algorithm eye-tracking human-computer interface physiological signals pupil size

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