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Taghia, Jalil
Publications (10 of 21) Show all publications
Leijon, A., von Gablenz, P., Holube, I., Taghia, J. & Smeds, K. (2023). Bayesian analysis of Ecological Momentary Assessment (EMA) data collected in adults before and after hearing rehabilitation. Frontiers in Digital Health, 5, Article ID 1100705.
Open this publication in new window or tab >>Bayesian analysis of Ecological Momentary Assessment (EMA) data collected in adults before and after hearing rehabilitation
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2023 (English)In: Frontiers in Digital Health, E-ISSN 2673-253X, Vol. 5, article id 1100705Article in journal (Refereed) Published
Abstract [en]

This paper presents a new Bayesian method for analyzing Ecological Momentary Assessment (EMA) data and applies this method in a re-analysis of data from a previous EMA study. The analysis method has been implemented as a freely available Python package EmaCalc, RRID:SCR 022943. The analysis model can use EMA input data including nominal categories in one or more situation dimensions, and ordinal ratings of several perceptual attributes. The analysis uses a variant of ordinal regression to estimate the statistical relation between these variables. The Bayesian method has no requirements related to the number of participants or the number of assessments by each participant. Instead, the method automatically includes measures of the statistical credibility of all analysis results, for the given amount of data. For the previously collected EMA data, the analysis results demonstrate how the new tool can handle heavily skewed, scarce, and clustered data that were collected on ordinal scales, and present results on interval scales. The new method revealed results for the population mean that were similar to those obtained in the previous analysis by an advanced regression model. The Bayesian approach automatically estimated the inter-individual variability in the population, based on the study sample, and could show some statistically credible intervention results also for an unseen random individual in the population. Such results may be interesting, for example, if the EMA methodology is used by a hearing-aid manufacturer in a study to predict the success of a new signal-processing method among future potential customers.

Place, publisher, year, edition, pages
Frontiers Media SA, 2023
Keywords
ambulatory assessment, Bayesian inference, Ecological Momentary Assessment, EMA, experience sampling, nominal data, ordinal data
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-331101 (URN)10.3389/fdgth.2023.1100705 (DOI)001030200300001 ()36874366 (PubMedID)2-s2.0-85149934062 (Scopus ID)
Note

QC 20230705

Available from: 2023-07-05 Created: 2023-07-05 Last updated: 2024-01-08Bibliographically approved
Taghia, J., Martin, R. & Leijon, A. (2020). An investigation on mutual information for the linear predictive system and the extrapolation of speech signals. In: Sprachkommunikation - 10. ITG-Fachtagung: . Paper presented at 10. ITG-Fachtagung Sprachkommunikation - 10th ITG Conference on Speech Communication, 26 September 2012 through 28 September 2012 (pp. 227-230). VDE Verlag GmbH
Open this publication in new window or tab >>An investigation on mutual information for the linear predictive system and the extrapolation of speech signals
2020 (English)In: Sprachkommunikation - 10. ITG-Fachtagung, VDE Verlag GmbH , 2020, p. 227-230Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Mutual information (MI) is an important information theoretic concept which has many applications in telecommunications, in blind source separation, and in machine learning. More recently, it has been also employed for the instrumental assessment of speech intelligibility where traditionally correlation based measures are used. In this paper, we address the difference between MI and correlation from the viewpoint of discovering dependencies between variables in the context of speech signals. We perform our investigation by considering the linear predictive approximation and the extrapolation of speech signals as examples. We compare a parametric MI estimation approach based on a Gaussian mixture model (GMM) with the k-nearest neighbor (KNN) approach which is a well-known non-parametric method available to estimate the MI. We show that the GMM-based MI estimator leads to more consistent results.

Place, publisher, year, edition, pages
VDE Verlag GmbH, 2020
Keywords
Blind source separation, Extrapolation, Gaussian distribution, Information theory, Nearest neighbor search, Speech intelligibility, Estimation approaches, Gaussian Mixture Model, K nearest neighbor (KNN), Mutual informations, Nonparametric methods, Predictive systems, Speech signals, Speech communication
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-285329 (URN)2-s2.0-85091334140 (Scopus ID)
Conference
10. ITG-Fachtagung Sprachkommunikation - 10th ITG Conference on Speech Communication, 26 September 2012 through 28 September 2012
Note

QC 20201202

Available from: 2020-12-02 Created: 2020-12-02 Last updated: 2024-03-18Bibliographically approved
Ma, Z., Kim, S., Martinez-Gomez, P., Taghia, J., Song, Y.-Z. & Gao, H. (2020). IEEE Access Special Section Editorial: AI-Driven Big Data Processing: Theory, Methodology, and Applications. IEEE Access, 8, 199882-199898
Open this publication in new window or tab >>IEEE Access Special Section Editorial: AI-Driven Big Data Processing: Theory, Methodology, and Applications
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2020 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 199882-199898Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2020
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-287514 (URN)10.1109/ACCESS.2020.3035461 (DOI)000589771000001 ()2-s2.0-85102904291 (Scopus ID)
Note

Not duplicate with DiVA 1455806

QC 20210303

Available from: 2021-03-03 Created: 2021-03-03 Last updated: 2024-03-18Bibliographically approved
Taghia, J. & Leijon, A. (2016). Variational Inference for Watson Mixture Model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(9), 1886-1900
Open this publication in new window or tab >>Variational Inference for Watson Mixture Model
2016 (English)In: IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 38, no 9, p. 1886-1900Article in journal (Refereed) Published
Abstract [en]

This paper addresses modelling data using the Watson distribution. The Watson distribution is one of the simplest distributions for analyzing axially symmetric data. This distribution has gained some attention in recent years due to its modeling capability. However, its Bayesian inference is fairly understudied due to difficulty in handling the normalization factor. Recent development of Markov chain Monte Carlo (MCMC) sampling methods can be applied for this purpose. However, these methods can be prohibitively slow for practical applications. A deterministic alternative is provided by variational methods that convert inference problems into optimization problems. In this paper, we present a variational inference for Watson mixture models. First, the variational framework is used to side-step the intractability arising from the coupling of latent states and parameters. Second, the variational free energy is further lower bounded in order to avoid intractable moment computation. The proposed approach provides a lower bound on the log marginal likelihood and retains distributional information over all parameters. Moreover, we show that it can regulate its own complexity by pruning unnecessary mixture components while avoiding over-fitting. We discuss potential applications of the modeling with Watson distributions in the problem of blind source separation, and clustering gene expression data sets.

Place, publisher, year, edition, pages
IEEE Computer Society, 2016
Keywords
Bayesian inference, variational inference, Watson distribution, mixture model, axially symmetric, clustering on the unit hypersphere, blind source separation, gene expression
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-193190 (URN)10.1109/TPAMI.2015.2498935 (DOI)000381432700013 ()26571512 (PubMedID)2-s2.0-84981276074 (Scopus ID)
Note

QC 20161012

Available from: 2016-10-12 Created: 2016-09-30 Last updated: 2024-03-18Bibliographically approved
Rana, P. K., Taghia, J., Ma, Z. & Flierl, M. (2015). Probabilistic Multiview Depth Image Enhancement Using Variational Inference. IEEE Journal on Selected Topics in Signal Processing, 9(3), 435-448
Open this publication in new window or tab >>Probabilistic Multiview Depth Image Enhancement Using Variational Inference
2015 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 9, no 3, p. 435-448Article in journal (Refereed) Published
Abstract [en]

An inference-based multiview depth image enhancement algorithm is introduced and investigated in this paper. Multiview depth imagery plays a pivotal role in free-viewpoint television. This technology requires high-quality virtual view synthesis to enable viewers to move freely in a dynamic real world scene. Depth imagery of different viewpoints is used to synthesize an arbitrary number of novel views. Usually, the depth imagery is estimated individually by stereo-matching algorithms and, hence, shows inter-view inconsistency. This inconsistency affects the quality of view synthesis negatively. This paper enhances the multiview depth imagery at multiple viewpoints by probabilistic weighting of each depth pixel. First, our approach classifies the color pixels in the multiview color imagery. Second, using the resulting color clusters, we classify the corresponding depth values in the multiview depth imagery. Each clustered depth image is subject to further subclustering. Clustering based on generative models is used for assigning probabilistic weights to each depth pixel. Finally, these probabilistic weights are used to enhance the depth imagery at multiple viewpoints. Experiments show that our approach consistently improves the quality of virtual views by 0.2 dB to 1.6 dB, depending on the quality of the input multiview depth imagery.

Keywords
Bayes methods, Cameras, Clustering algorithms, Image color analysis, Sensors, Signal processing algorithms, Vectors, Dirichlet mixture model, Multiview video, free-viewpoint television, multiview depth consistency, virtual view synthesis
National Category
Signal Processing
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-163409 (URN)10.1109/JSTSP.2014.2373331 (DOI)000351749800006 ()2-s2.0-84938918970 (Scopus ID)
Note

QC 20150402

Available from: 2015-04-01 Created: 2015-04-01 Last updated: 2024-03-18Bibliographically approved
Ma, Z., Rana, P. K., Taghia, J., Flierl, M. & Leijon, A. (2014). Bayesian estimation of Dirichlet mixture model with variational inference. Pattern Recognition, 47(9), 3143-3157
Open this publication in new window or tab >>Bayesian estimation of Dirichlet mixture model with variational inference
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2014 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 47, no 9, p. 3143-3157Article in journal (Refereed) Published
Abstract [en]

In statistical modeling, parameter estimation is an essential and challengeable task. Estimation of the parameters in the Dirichlet mixture model (DMM) is analytically intractable, due to the integral expressions of the gamma function and its corresponding derivatives. We introduce a Bayesian estimation strategy to estimate the posterior distribution of the parameters in DMM. By assuming the gamma distribution as the prior to each parameter, we approximate both the prior and the posterior distribution of the parameters with a product of several mutually independent gamma distributions. The extended factorized approximation method is applied to introduce a single lower-bound to the variational objective function and an analytically tractable estimation solution is derived. Moreover, there is only one function that is maximized during iterations and, therefore, the convergence of the proposed algorithm is theoretically guaranteed. With synthesized data, the proposed method shows the advantages over the EM-based method and the previously proposed Bayesian estimation method. With two important multimedia signal processing applications, the good performance of the proposed Bayesian estimation method is demonstrated.

Keywords
Bayesian estimation, Variational inference, Extended factorized approximation, Relative convexity, Dirichlet distribution, Gamma prior, Mixture modeling, LSF quantization, Multiview depth image enhancement
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-147714 (URN)10.1016/j.patcog.2014.04.002 (DOI)000336872000028 ()2-s2.0-84900821630 (Scopus ID)
Note

QC 20140707

Available from: 2014-07-07 Created: 2014-07-03 Last updated: 2024-03-18Bibliographically approved
Taghia, J., Ma, Z. & Leijon, A. (2014). Bayesian Estimation of the von-Mises Fisher Mixture Model with Variational Inference. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(9), 1701-1715
Open this publication in new window or tab >>Bayesian Estimation of the von-Mises Fisher Mixture Model with Variational Inference
2014 (English)In: IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 36, no 9, p. 1701-1715Article in journal (Refereed) Published
Abstract [en]

This paper addresses the Bayesian estimation of the von-Mises Fisher (vMF) mixture model with variational inference (VI). The learning task in VI consists of optimization of the variational posterior distribution. However, the exact solution by VI does not lead to an analytically tractable solution due to the evaluation of intractable moments involving functional forms of the Bessel function in their arguments. To derive a closed-form solution, we further lower bound the evidence lower bound where the bound is tight at one point in the parameter distribution. While having the value of the bound guaranteed to increase during maximization, we derive an analytically tractable approximation to the posterior distribution which has the same functional form as the assigned prior distribution. The proposed algorithm requires no iterative numerical calculation in the re-estimation procedure, and it can potentially determine the model complexity and avoid the over-fitting problem associated with conventional approaches based on the expectation maximization. Moreover, we derive an analytically tractable approximation to the predictive density of the Bayesian mixture model of vMF distributions. The performance of the proposed approach is verified by experiments with both synthetic and real data.

Keywords
Bayesian estimation, von-Mises Fisher distribution, mixture model, variational inference, directional distribution, predictive density, gene expressions, speaker identification
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-150510 (URN)10.1109/TPAMI.2014.2306426 (DOI)000340210100001 ()26352226 (PubMedID)2-s2.0-84905593212 (Scopus ID)
Note

QC 20140916

Available from: 2014-09-16 Created: 2014-09-05 Last updated: 2024-03-18Bibliographically approved
Ma, Z., Teschendorff, A. E., Yu, H., Taghia, J. & Guo, J. (2014). Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis. International Journal of Molecular Sciences, 15(6), 10835-10854
Open this publication in new window or tab >>Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis
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2014 (English)In: International Journal of Molecular Sciences, ISSN 1661-6596, E-ISSN 1422-0067, Vol. 15, no 6, p. 10835-10854Article in journal (Refereed) Published
Abstract [en]

As a key regulatory mechanism of gene expression, DNA methylation patterns are widely altered in many complex genetic diseases, including cancer. DNA methylation is naturally quantified by bounded support data; therefore, it is non-Gaussian distributed. In order to capture such properties, we introduce some non-Gaussian statistical models to perform dimension reduction on DNA methylation data. Afterwards, non-Gaussian statistical model-based unsupervised clustering strategies are applied to cluster the data. Comparisons and analysis of different dimension reduction strategies and unsupervised clustering methods are presented. Experimental results show that the non-Gaussian statistical model-based methods are superior to the conventional Gaussian distribution-based method. They are meaningful tools for DNA methylation analysis. Moreover, among several non-Gaussian methods, the one that captures the bounded nature of DNA methylation data reveals the best clustering performance.

Keywords
non-Gaussian statistical models, dimension reduction, unsupervised learning, feature selection, DNA methylation analysis
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-148635 (URN)10.3390/ijms150610835 (DOI)000338639000097 ()24937687 (PubMedID)2-s2.0-84902578435 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, 612212
Note

QC 20140811

Available from: 2014-08-11 Created: 2014-08-11 Last updated: 2024-03-18Bibliographically approved
Taghia, J. & Leijon, A. (2014). Separation of Unknown Number of Sources. IEEE Signal Processing Letters, 21(5), 625-629
Open this publication in new window or tab >>Separation of Unknown Number of Sources
2014 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 21, no 5, p. 625-629Article in journal (Refereed) Published
Abstract [en]

We address the problem of blind source separation in acoustic applications where there is no prior knowledge about the number of mixing sources. The presented method employs a mixture of complex Watson distributions in its generative model with a sparse Dirichlet distribution over the mixture weights. The problem is formulated in a fully Bayesian inference with assuming prior distributions over all model parameters. The presented model can regulate its own complexity by pruning unnecessary components by which we can possibly relax the assumption of prior knowledge on the number of sources.

Keywords
Bayesian inference, Blind source separation, Complex Watson distribution, Variational inference
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-153744 (URN)10.1109/LSP.2014.2309607 (DOI)000347922600001 ()2-s2.0-84897498048 (Scopus ID)
Note

QC 20141007

Available from: 2014-10-08 Created: 2014-10-08 Last updated: 2024-03-18Bibliographically approved
Rana, P. K., Taghia, J. & Flier, M. (2014). Statistical methods for inter-view depth enhancement. In: 2014 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON). Paper presented at 3DTV-Conference on True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), JUL 02-04, 2014, Budapest, Hungary (pp. 6874755). IEEE
Open this publication in new window or tab >>Statistical methods for inter-view depth enhancement
2014 (English)In: 2014 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), IEEE , 2014, p. 6874755-Conference paper, Published paper (Refereed)
Abstract [en]

This paper briefly presents and evaluates recent advances in statistical methods for improving inter-view inconsistency in multiview depth imagery. View synthesis is vital in free-viewpoint television in order to allow viewers to move freely in a dynamic scene. Here, depth image-based rendering plays a pivotal role by synthesizing an arbitrary number of novel views by using a subset of captured views and corresponding depth maps only. Usually, each depth map is estimated individually at different viewpoints by stereo matching and, hence, shows lack of inter-view consistency. This lack of consistency affects the quality of view synthesis negatively. This paper discusses two different approaches to enhance the inter-view depth consistency. The first one uses generative models based on multiview color and depth classification to assign a probabilistic weight to each depth pixel. The weighted depth pixels are utilized to enhance depth maps. The second one performs inter-view consistency testing in depth difference space to enhance the depth maps at multiple viewpoints. We comparatively evaluate these two methods and discuss their pros and cons for future work.

Place, publisher, year, edition, pages
IEEE, 2014
Series
3DTV Conference, ISSN 2161-2021
Keywords
Multiview depth maps, depth map enhancement, inter-view consistency, variational Bayesian inference
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-158300 (URN)10.1109/3DTV.2014.6874755 (DOI)000345738600045 ()2-s2.0-84906569216 (Scopus ID)978-1-4799-4758-4 (ISBN)
Conference
3DTV-Conference on True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), JUL 02-04, 2014, Budapest, Hungary
Note

QC 20150109

Available from: 2015-01-09 Created: 2015-01-07 Last updated: 2024-03-18Bibliographically approved
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