| 1. |
- Taghia, Jalal, et al.
(författare)
-
An Evaluation of noise power spectral density estimation algorithms in adverse acoustic environments
- 2011
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Ingår i: 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011. - : IEEE. ; , s. 4640-4643
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Konferensbidrag (refereegranskat)abstract
- Noise power spectral density estimation is an important componentof speech enhancement systems due to its considerable effect onthe quality and the intelligibility of the enhanced speech. Recently,many new algorithms have been proposed and significant progressin noise tracking has been made.In this paper, we present an evaluation framework for measuringthe performance of some recently proposed and some well-knownnoise power spectral density estimators and compare their performancein adverse acoustic environments. In this investigation we donot only consider the performance in the mean of a spectral distancemeasure but also evaluate the variance of the estimators as the latteris related to undesirable fluctuations also known as musical noise.By providing a variety of different non-stationary noises, the robustnessof noise estimators in adverse environments is examined.
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| 2. |
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| 3. |
- Taghia, J., et al.
(författare)
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An investigation on mutual information for the linear predictive system and the extrapolation of speech signals
- 2012
-
Ingår i: Proceedings of 10th ITG Symposium on Speech Communication. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- 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.
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| 4. |
- Taghia, Jalal, et al.
(författare)
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Dual-channel noise reduction based on a mixture of circular-symmetric complex Gaussians on unit hypersphere
- 2013
-
Ingår i: ICASSP IEEE Int Conf Acoust Speech Signal Process Proc. - 9781479903566 ; , s. 7289-7293
-
Konferensbidrag (refereegranskat)abstract
- In this paper a model-based dual-channel noise reduction approach is presented which is an alternative to conventional noise reduction algorithms essentially due to its independence of the noise power spectral density estimation and of any prior knowledge about the spatial noise field characteristics. We use a mixture of circular-symmetric complex-Gaussian distributions projected on the unit hypersphere for modeling the complex discrete Fourier transform coefficients of noisy speech signals in the frequency domain. According to the derived mixture model, clustering of the noise and the target speech components is performed depending on their direction of arrival. A soft masking strategy is proposed for speech enhancement based on responsibilities assigned to the target speech class in each time-frequency bin. Our experimental results show that the proposed approach is more robust than conventional dual-channel noise reduction systems based on the single- and dual-channel noise power spectral density estimators.
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| 5. |
- Bånkestad, Maria, et al.
(författare)
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Variational Elliptical Processes
- 2023
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Ingår i: Transactions on Machine Learning Research. - 2835-8856.
-
Tidskriftsartikel (refereegranskat)abstract
- We present elliptical processes—a family of non-parametric probabilistic models that subsumes Gaussian processes and Student's t processes. This generalization includes a range of new heavy-tailed behaviors while retaining computational tractability. Elliptical processes are based on a representation of elliptical distributions as a continuous mixture of Gaussian distributions. We parameterize this mixture distribution as a spline normalizing flow, which we train using variational inference. The proposed form of the variational posterior enables a sparse variational elliptical process applicable to large-scale problems. We highlight advantages compared to Gaussian processes through regression and classification experiments. Elliptical processes can supersede Gaussian processes in several settings, including cases where the likelihood is non-Gaussian or when accurate tail modeling is essential.
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| 6. |
- Cai, Weidong, et al.
(författare)
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A multi-demand operating system underlying diverse cognitive tasks
- 2024
-
Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- The existence of a multiple-demand cortical system with an adaptive, domain-general, role in cognition has been proposed, but the underlying dynamic mechanisms and their links to cognitive control abilities are poorly understood. Here we use a probabilistic generative Bayesian model of brain circuit dynamics to determine dynamic brain states across multiple cognitive domains, independent datasets, and participant groups, including task fMRI data from Human Connectome Project, Dual Mechanisms of Cognitive Control study and a neurodevelopment study. We discover a shared brain state across seven distinct cognitive tasks and found that the dynamics of this shared brain state predicted cognitive control abilities in each task. Our findings reveal the flexible engagement of dynamic brain processes across multiple cognitive domains and participant groups, and uncover the generative mechanisms underlying the functioning of a domain-general cognitive operating system. Our computational framework opens promising avenues for probing neurocognitive function and dysfunction. A consistent set of brain areas is engaged across diverse cognitive tasks. Here, the authors reveal a unifying latent brain state that predicts performance across seven tasks, linking a core control network to cognitive flexibility and adaptive behaviors.
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| 7. |
- Hongmei, Hu, et al.
(författare)
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Sparsity level in a non-negative matrix factorization based speech strategy in cochlear implants
- 2012
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Ingår i: 2012 Proceedings Of The 20th European Signal Processing Conference (EUSIPCO). - : IEEE Computer Society. - 9781467310680 ; , s. 2432-2436
-
Konferensbidrag (refereegranskat)abstract
- Non-negative matrix factorization (NMF) has increasinglybeen used as a tool in signal processing in the last years, butit has not been used in the cochlear implants (CIs). Toimprove the performance of CIs in noisy environments, anovel sparse strategy is proposed by applying NMF onenvelops of 22 channels. In the new algorithm, the noisyspeech is first transferred to the time-frequency domain viaa 22- channel filter bank and the envelope in each frequencychannel is extracted; secondly, NMF is applied to theenvelope matrix (envelopegram); finally, the sparsitycondition is applied to the coefficient matrix to get moresparse representation. Speech reception threshold (SRT)subjective experiment was performed in combination withfive objective measurements in order to choose the properparameters for the sparse NMF model.
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| 8. |
- Hu, H., et al.
(författare)
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Speech enhancement via combination of Wiener filter and blind source separation
- 2011
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Ingår i: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China (ISKE2011). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783642256578 ; , s. 485-494
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Konferensbidrag (refereegranskat)abstract
- Automatic speech recognition (ASR) often fails in acoustically noisy environments. Aimed to improve speech recognition scores of an ASR in a real-life like acoustical environment, a speech pre-processing system is proposed in this paper, which consists of several stages: First, a convolutive blind source separation (BSS) is applied to the spectrogram of the signals that are pre-processed by binaural Wiener filtering (BWF). Secondly, the target speech is detected by an ASR system recognition rate based on a Hidden Markov Model (HMM). To evaluate the performance of the proposed algorithm, the signal-to-interference ratio (SIR), the improvement signal-to-noise ratio (ISNR) and the speech recognition rates of the output signals were calculated using the signal corpus of the CHiME database. The results show an improvement in SIR and ISNR, but no obvious improvement of speech recognition scores. Improvements for future research are suggested.
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| 9. |
- Jalil, Taghia, et al.
(författare)
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Variational Inference for Watson Mixture Model
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 0162-8828 .- 1939-3539.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- This paper addresses modelling data using the multivariate Watson distributions. The Watson distribution is one of thesimplest distributions for analyzing axially symmetric data. This distribution has gained some attention in recent years due to itsmodeling capability. However, its Bayesian inference is fairly understudied due to difficulty in handling the normalization factor. Recentdevelopment of Monte-Carlo Markov chain (MCMC) sampling methods can be applied for this purpose. However, these methods canbe prohibitively slow for practical applications. A deterministic alternative is provided by variational methods that convert inferenceproblems into optimization problems. In this paper, we present a variational inference for Watson mixture model. First, the variationalframework is used to side-step the intractability arising from the coupling of latent states and parameters. Second, the variational freeenergy is further lower bounded in order to avoid intractable moment computation. The proposed approach provides a lower bound onthe log marginal likelihood and retains distributional information over all parameters. Moreover, we show that it can regulate its owncomplexity by pruning unnecessary mixture components while avoiding over-fitting. We discuss potential applications of the modelingwith Watson distributions in the problem of blind source separation, and clustering gene expression data sets.
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| 10. |
- John, Wolfgang, et al.
(författare)
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ANIARA Project - Automation of Network Edge Infrastructure and Applications with Artificial Intelligence
- 2023
-
Ingår i: Ada User Journal. - 1381-6551. ; 42:2, s. 92-95
-
Tidskriftsartikel (refereegranskat)abstract
- Emerging use-cases like smart manufacturing and smart cities pose challenges in terms of latency, which cannot be satisfied by traditional centralized infrastructure. Edge networks, which bring computational capacity closer to the users/clients, are a promising solution for supporting these critical low latency services. Different from traditional centralized networks, the edge is distributed by nature and is usually equipped with limited compute capacity. This creates a complex network to handle, subject to failures of different natures, that requires novel solutions to work in practice. To reduce complexity, edge application technology enablers, advanced infrastructure and application orchestration techniques need to be in place where AI and ML are key players.
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| 11. |
- Larsson, Hannes, et al.
(författare)
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Source Selection in Transfer Learning for Improved Service Performance Predictions
- 2021
-
Ingår i: 2021 IFIP Networking Conference and Workshops (IFIP Networking). - : Institute of Electrical and Electronics Engineers (IEEE). - 9783903176393 - 9781665445016
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Konferensbidrag (refereegranskat)abstract
- Learning performance models for network and cloud services is challenging due to the dynamics of the operational environment stemming from network changes, and scaling and migration decisions in the cloud. This requires exchange or adaptation of the models in order to maintain prediction accuracy over time. Approaches that incorporate previously acquired knowledge using transfer learning is a viable technique for timely and robust model adaptation, especially when the training data is limited. In this paper, we study the challenge of source selection in transfer learning for improved service performance prediction. We quantify the impact of different source domains on the accuracy of a target model in another domain. The evaluation is performed using data traces obtained from a testbed that runs a Video-on-Demand service and a Key-Value Store under various load conditions. We find that the choice of source domain can yield a transfer gain, and sometimes a substantial transfer penalty. To mitigate this, we propose and evaluate two source-selection approaches with the aim of selecting a source domain with relevant knowledge for the target domain. A key result is that such source selection should encourage source-domain diversity rather than domain similarity in scenarios with few samples in the target domain.
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| 12. |
- Larsson, Hannes, et al.
(författare)
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Towards Source Selection in Transfer Learning for Cloud Performance Prediction
- 2021
-
Ingår i: 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM). - : IEEE. - 9783903176324 - 9781728190419 ; , s. 599-603
-
Konferensbidrag (refereegranskat)abstract
- Learning performance models for cloud services is challenging due to the dynamics of the operational environment stemming from scaling and migration decisions. This requires exchange or adaptation of the models in order to maintain prediction accuracy over time. Approaches that incorporates previously acquired knowledge using transfer learning is a viable technique for timely and robust model adaptation, especially when the training data is limited. In this paper we quantify the impact of different source domains on the accuracy of a target model in another domain. The evaluation is performed using data traces collected from a testbed that runs a Video-on-Demand service and a Key-Value Store under various load conditions. We find that the choice of source domain can yield a transfer gain, and sometimes a substantial transfer penalty.
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| 13. |
- Leijon, Arne, et al.
(författare)
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Bayesian analysis of Ecological Momentary Assessment (EMA) data collected in adults before and after hearing rehabilitation
- 2023
-
Ingår i: Frontiers in Digital Health. - : Frontiers Media SA. - 2673-253X. ; 5
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 14. |
- Ma, Zhanyu, et al.
(författare)
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Bayesian estimation of Dirichlet mixture model with variational inference
- 2014
-
Ingår i: Pattern Recognition. - : Elsevier BV. - 0031-3203 .- 1873-5142. ; 47:9, s. 3143-3157
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 15. |
- Ma, Zhanyu, et al.
(författare)
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Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis
- 2014
-
Ingår i: International Journal of Molecular Sciences. - : MDPI AG. - 1661-6596 .- 1422-0067. ; 15:6, s. 10835-10854
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 16. |
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| 17. |
- Ma, Zhanyu, et al.
(författare)
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Insights Into Multiple/Single Lower Bound Approximation for Extended Variational Inference in Non-Gaussian Structured Data Modeling
- 2020
-
Ingår i: IEEE Transactions on Neural Networks and Learning Systems. - 2162-237X .- 2162-2388. ; 31:7, s. 2240-2254
-
Tidskriftsartikel (refereegranskat)abstract
- For most of the non-Gaussian statistical models, the data being modeled represent strongly structured properties, such as scalar data with bounded support (e.g., beta distribution), vector data with unit length (e.g., Dirichlet distribution), and vector data with positive elements (e.g., generalized inverted Dirichlet distribution). In practical implementations of non-Gaussian statistical models, it is infeasible to find an analytically tractable solution to estimating the posterior distributions of the parameters. Variational inference (VI) is a widely used framework in Bayesian estimation. Recently, an improved framework, namely, the extended VI (EVI), has been introduced and applied successfully to a number of non-Gaussian statistical models. EVI derives analytically tractable solutions by introducing lower bound approximations to the variational objective function. In this paper, we compare two approximation strategies, namely, the multiple lower bounds (MLBs) approximation and the single lower bound (SLB) approximation, which can be applied to carry out the EVI. For implementation, two different conditions, the weak and the strong conditions, are discussed. Convergence of the EVI depends on the selection of the lower bound, regardless of the choice of weak or strong condition. We also discuss the convergence properties to clarify the differences between MLB and SLB. Extensive comparisons are made based on some EVI-based non-Gaussian statistical models. Theoretical analysis is conducted to demonstrate the differences between the weak and strong conditions. Experimental results based on real data show advantages of the SLB approximation over the MLB approximation.
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| 18. |
- Ma, Zhanyu, et al.
(författare)
-
Line spectral frequencies modeling by a mixture of von Mises-Fisher distributions
- 2015
-
Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 114, s. 219-224
-
Tidskriftsartikel (refereegranskat)abstract
- Efficient quantization of the linear predictive coding (LPC) parameters plays a key role in parametric speech coding. The line spectral frequency (LSF) representation of the LPC parameters has found its applications in speech model quantization. In practical implementations of vector quantization (VQ), probability density function optimized VQ has been shown to be more efficient than the VQ based on training data. In this paper, we present the LSF parameters by a unit vector form, which has directional characteristics. The underlying distribution of this unit vector variable is modeled by a von Mises-Fisher mixture model (VMM). An optimal inter-component bit allocation strategy is proposed based on high rate theory and a distortion-rate (D-R) relation is derived for the VMM based-VQ (VVQ). Experimental results show that the VVQ outperforms the recently introduced Dirichlet mixture model-based VQ and the conventional Gaussian mixture model-based VQ in terms of modeling performance and D-R relation.
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| 19. |
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| 20. |
- Mohammadiha, Nasser, et al.
(författare)
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Single channel speech enhancement using Bayesian NMF with recursive temporal updates of prior distributions
- 2012
-
Ingår i: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012. - : IEEE conference proceedings. - 9781467300452 ; , s. 4561-4564
-
Konferensbidrag (refereegranskat)abstract
- We present a speech enhancement algorithm which is based on a Bayesian Nonnegative Matrix Factorization (NMF). Both Minimum Mean Square Error (MMSE) and Maximum a-Posteriori (MAP) estimates of the magnitude of the clean speech DFT coefficients are derived. To exploit the temporal continuity of the speech and noise signals, a proper prior distribution is introduced by widening the posterior distribution of the NMF coefficients at the previous time frames. To do so, a recursive temporal update scheme is proposed to obtain the mean value of the prior distribution; also, the uncertainty of the prior information is governed by the shape parameter of the distribution which is learnt automatically based on the nonstationarity of the signals. Simulations show a considerable improvement compared to the maximum likelihood NMF based speech enhancement algorithm for different input SNRs.
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| 21. |
- Rana, Pravin Kumar, 1982-, et al.
(författare)
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A Variational Bayesian Inference Framework for Multiview Depth Image Enhancement
- 2012
-
Ingår i: Proceedings - 2012 IEEE International Symposium on Multimedia, ISM 2012. - : IEEE. - 9780769548753 ; , s. 183-190
-
Konferensbidrag (refereegranskat)abstract
- In this paper, a general model-based framework for multiview depth image enhancement is proposed. Depth imagery plays a pivotal role in emerging 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 lack of inter-view consistency. This inconsistency affects the quality of view synthesis negatively. This paper enhances the inter-view consistency of multiview depth imagery by using a variational Bayesian inference framework. First, our approach classifies the color information 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. Finally, the resulting mean of the sub-clusters is used to enhance the depth imagery at multiple viewpoints. Experiments show that our approach improves the quality of virtual views by up to 0.25 dB.
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| 22. |
- Rana, Pravin Kumar, 1982-, et al.
(författare)
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Multiview Depth Map Enhancement by Variational Bayes Inference Estimation of Dirichlet Mixture Models
- 2013
-
Ingår i: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - : IEEE. - 9781479903566 ; , s. 1528-1532
-
Konferensbidrag (refereegranskat)abstract
- High quality view synthesis is a prerequisite for future free-viewpointtelevision. It will enable viewers to move freely in a dynamicreal world scene. Depth image based rendering algorithms willplay a pivotal role when synthesizing an arbitrary number of novelviews by using a subset of captured views and corresponding depthmaps only. Usually, each depth map is estimated individually bystereo-matching algorithms and, hence, shows lack of inter-viewconsistency. This inconsistency affects the quality of view synthesis negatively. This paper enhances the inter-view consistency ofmultiview depth imagery. First, our approach classifies the colorinformation in the multiview color imagery by modeling color witha mixture of Dirichlet distributions where the model parameters areestimated in a Bayesian framework with variational inference. Second, using the resulting color clusters, we classify the correspondingdepth values in the multiview depth imagery. Each clustered depthimage is subject to further sub-clustering. Finally, the resultingmean of each sub-cluster is used to enhance the depth imagery atmultiple viewpoints. Experiments show that our approach improvesthe average quality of virtual views by up to 0.8 dB when comparedto views synthesized by using conventionally estimated depth maps.
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| 23. |
- Rana, Pravin Kumar, et al.
(författare)
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Probabilistic Multiview Depth Image Enhancement Using Variational Inference
- 2015
-
Ingår i: IEEE Journal on Selected Topics in Signal Processing. - 1932-4553 .- 1941-0484. ; 9:3, s. 435-448
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 24. |
- Rana, Pravin Kumar, et al.
(författare)
-
Statistical methods for inter-view depth enhancement
- 2014
-
Ingår i: 2014 3DTV-Conference. - : IEEE. - 9781479947584 ; , s. 6874755-
-
Konferensbidrag (refereegranskat)abstract
- 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.
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| 25. |
- Taghia, Jalil, et al.
(författare)
-
A variational Bayes approach to the underdetermined blind source separation with automatic determination of the number of sources
- 2012
-
Ingår i: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. - : IEEE. - 9781467300469 ; , s. 253-256
-
Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a variational Bayes approach to the underdetermined blind source separation and show how a variational treatment can open up the possibility of determining the actual number of sources. The procedure is performed in a frequency bin-wise manner. In every frequency bin, we model the time-frequency mixture by a variational mixture of Gaussians with a circular-symmetric complex-Gaussian density function. In the Bayesian inference, we consider appropriate conjugate prior distributions for modeling the parameters of this distribution. The learning task consists of estimating the hyper-parameters characterizing the parameter distributions for the optimization of the variational posterior distribution. The proposed approach requires no prior knowledge on the number of sources in a mixture.
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| 26. |
- Taghia, Jalil, et al.
(författare)
-
An investigation on mutual information for the linear predictive system and the extrapolation of speech signals
- 2020
-
Ingår i: Sprachkommunikation - 10. ITG-Fachtagung. - : VDE Verlag GmbH. ; , s. 227-230
-
Konferensbidrag (refereegranskat)abstract
- 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.
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| 27. |
- Taghia, Jalil, et al.
(författare)
-
Bayesian Estimation of the von-Mises Fisher Mixture Model with Variational Inference
- 2014
-
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 0162-8828 .- 1939-3539. ; 36:9, s. 1701-1715
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 28. |
- Taghia, Jalil
(författare)
-
Bayesian Modeling of Directional Data with Acoustic and Other Applications
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A direction is defined here as a multi-dimensional unit vector. Such unitvectors form directional data. Closely related to directional data are axialdata for which each direction is equivalent to the opposite direction.Directional data and axial data arise in various fields of science. In probabilisticmodeling of such data, probability distributions are needed whichcount for the structure of the space from which data samples are collected.Such distributions are known as directional distributions and axial distributions.This thesis studies the von Mises-Fisher (vMF) distribution and the(complex) Watson distribution as representatives of directional and axialdistributions.Probabilistic models of the data are defined through a set of parameters.In the Bayesian view to uncertainty, these parameters are regarded as randomvariables in the learning inference. The primary goal of this thesis is todevelop Bayesian inference for directional and axial models, more precisely,vMF and (complex) Watson distributions, and parametric mixture modelsof such distributions. The Bayesian inference is realized using a family ofoptimization methods known as variational inference. With the proposedvariational methods, the intractable Bayesian inference problem is cast asan optimization problem.The variational inference for vMF andWatson models shall open up newapplications and advance existing application domains by reducing restrictiveassumptions made by current modelling techniques. This is the centraltheme of the thesis in all studied applications. Unsupervised clustering ofgene-expression and gene-microarray data is an existing application domain,which has been further advanced in this thesis. This thesis also advancesapplication of the complex Watson models in the problem of blind sourceseparation (BSS) with acoustic applications. Specifically, it is shown thatthe restrictive assumption of prior knowledge on the true number of sourcescan be relaxed by the desirable pruning property in Bayesian learning, resultingin BSS methods which can estimate the number of sources.Furthermore, this thesis introduces a fully Bayesian recursive frameworkfor the BSS task. This is an attempt toward realization of an online BSSmethod. In order to reduce the well-known problem of permutation ambiguityin the frequency domain, the complete BSS problem is solved in one unified modeling step, combining the frequency bin-wise source estimationwith the permutation problem. To realize this, all time frames and frequencybins are connected using a first order Markov chain. The model cancapture dependencies across both time frames and frequency bins, simultaneously,using a feed-forward two-dimensional hidden Markov model (2-DHMM).
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| 29. |
- Taghia, Jalil, et al.
(författare)
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Bayesian Recursive Blind Source Separation
- 2024
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Ingår i: Journal of machine learning research. - : MIT Press. - 1532-4435 .- 1533-7928.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- We consider the problem of blind source separation (BSS) of convolutive mixtures in underdeterminedscenarios, where there are more sources to estimate than recorded signals. This problemhas been intensively studied in the literature. Many successful methods relay on batch processingof previously recorded signals, and hence are only best suited for noncausal systems. This paperaddresses the problem of online BSS. To realize this, we develop a Bayesian recursive framework.The proposed Bayesian framework allows incorporating prior knowledge in a coherentway, and therecursive learning allows to combine information gained from the current observation with all informationfromthe previous observations. Experiments using live audio recordings show promisingresults.
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| 30. |
- Taghia, Jalil, et al.
(författare)
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Blind Source Separation of Nondisjoint Sources in The Time-Frequency Domain with Model-Based Determination of Source Contribution
- 2011
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Ingår i: 2011 IEEE International Symposium On Signal Processing And Information Technology (ISSPIT). - New York : IEEE. - 9781467307536 ; , s. 276-280
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Konferensbidrag (refereegranskat)abstract
- While most blind source separation (BSS) algorithms rely on the assumption that at most one source is dominant at each time-frequency (TF) point, recently, two BSS approaches, [1], [2], have been proposed that allow multiple active sources at time-frequency (TF) points under certain assumptions. In both algorithms, the active sources in every single TF point are found by an exhaustive search through an optimization procedure which is computationally expensive. In this work, we address this limitation and avoid the exhaustive search by determining the source contribution in every TF point. The source contributions are expressed by a set of posterior probabilities. Hereby, we propose a model-based blind source separation algorithm that allows sources to be nondisjoint in the TF domain while being computationally more tractable. The proposed BSS approach is shown to be robust with respect to different reverberation times and microphone spacings.
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| 31. |
- Taghia, Jalil, et al.
(författare)
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Conditionally Independent Multiresolution Gaussian Processes
- 2019
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Ingår i: 22nd International Conference On Artificial Intelligence And Statistics, Vol 89.
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Konferensbidrag (refereegranskat)abstract
- The multiresolution Gaussian process (GP) has gained increasing attention as a viable approach towards improving the quality of approximations in GPs that scale well to large-scale data. Most of the current constructions assume full independence across resolutions. This assumption simplifies the inference, but it underestimates the uncertainties in transitioning from one resolution to another. This in turn results in models which are prone to overfitting in the sense of excessive sensitivity to the chosen resolution, and predictions which are non-smooth at the boundaries. Our contribution is a new construction which instead assumes conditional independence among GPs across resolutions. We show that relaxing the full independence assumption enables robustness against overfitting, and that it delivers predictions that are smooth at the boundaries. Our new model is compared against current state of the art on 2 synthetic and 9 real-world datasets. In most cases, our new conditionally independent construction performed favorably when compared against models based on the full independence assumption. In particular, it exhibits little to no signs of overfitting.
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| 32. |
- Taghia, Jalil, et al.
(författare)
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Demonstration of Policy-Induced Unsupervised Feature Selection in a 5G network
- 2022
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Ingår i: IEEE INFOCOM 2022 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665409261 - 9781665409278
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Konferensbidrag (refereegranskat)abstract
- A key enabler for integration of machine-learning models in network management is timely access to reliable data, in terms of features, which require pervasive measurement points throughout the network infrastructure. However, excessive measurements and monitoring is associated with network overhead. The demonstrator described in this paper shows key aspects of feature selection using a novel method based on unsupervised feature selection that provides a structured approach in incorporation of network-management domain knowledge in terms of policies. The demonstrator showcases the benefits of the approach in a 5G-mmWave network scenario where the model is trained to predict round-trip time as experienced by a user.
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| 33. |
- Taghia, Jalil, et al.
(författare)
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On von-Mises Fisher mixture model in Text-independent speaker identification
- 2013
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Ingår i: Proceedings of the 2013 INTERSPEECH. ; , s. 2499-2503
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Konferensbidrag (refereegranskat)abstract
- This paper addresses text-independent speaker identification (SI) based on line spectral frequencies (LSFs). The LSFs are transformed to differential LSFs (MLSF) in order to exploit their boundary and ordering properties. We show that the square root of MLSF has interesting directional characteristics implying that their distribution can be modeled by a mixture of von-Mises Fisher (vMF) distributions. We analytically estimate the mixture model parameters in a fully Bayesian treatment by using variational inference. In the Bayesian inference, we can potentially determine the model complexity and avoid overfitting problem associated with conventional approaches based on the expectation maximization. The experimental results confirm the effectiveness of the proposed SI system.
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| 34. |
- Taghia, Jalil, et al.
(författare)
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Separation of Unknown Number of Sources
- 2014
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Ingår i: IEEE Signal Processing Letters. - 1070-9908 .- 1558-2361. ; 21:5, s. 625-629
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 35. |
- Taghia, Jalil, et al.
(författare)
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Subband-based Single-channel Source Separation of Instantaneous Audio Mixtures
- 2009
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Ingår i: World Applied Sciences Journal. - : IDOSI Publications. - 1818-4952 .- 1991-6426. ; 6:6, s. 784-792
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a new algorithm is developed to separate the audio sources from a single instantaneous mixture. The algorithm is based on subband decomposition and uses a hybrid system of Empirical Mode Decomposition (EMD) and Principle Component Analysis (PCA) to construct artificial observations from the single mixture. In the separation stage of algorithm, we use Independent Component Analysis (ICA) to find independent components. At first the observed mixture is divided into a finite number of subbands through filtering with a parallel bank of FIR band-pass filters. Then EMD is employed to extract Intrinsic Mode Functions (IMFs) in each subband. By applying PCA to the extracted components, we find uncorrelated components which are the artificial observations. Then we obtain independent components by applying Independent Component Analysis (ICA) to the uncorrelated components. Finally, we carry out subband synthesis process to reconstruct fullband separated signals. The experimental results substantiate that the proposed method truly performs the task of source separation from a single instantaneous mixture.
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| 36. |
- Taghia, Jalil, et al.
(författare)
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Variational Inference for Watson Mixture Model
- 2016
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE Computer Society. - 0162-8828 .- 1939-3539. ; 38:9, s. 1886-1900
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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