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Variational probabilistic speech separation using microphone arrays

IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2007年第1期15卷 135-149页

作者： Rennie, Steven J. Aarabi, Parham Frey, Brendan J. Univ Toronto Dept Comp Engn Toronto ON M5S 3G4 Canada

Separating multiple speech sources using a limited number of noisy sensor measurements presents a difficult problem, but one that is of great practical interest. Although previously introduced source separation methods [such as independent component analysis (ICA)] can be made to work in many situations, most of these methods fail when the sensors are very noisy or when the number of sources exceeds the number of sensors. Our approach to this problem is to combine the multiple sensor likelihoods [obtained using time-delay-of-arrival (TDOA) information] with a generative probability model of the sources. This model accounts for the power spectrum of each source using a mixture model, and accounts for the phase of each source using one discretized hidden phase variable for each frequency. Source separation is achieved by identifying the source vector configuration of maximum a posteriori probability, given all available information. An exhaustive search for the MAP configuration is computationally intractable, but we present an efficient variational technique that performs approximate probabilistic inference. For the problem of separating delayed additive noise corrupted speech mixtures, the algorithm is able to improve upon the signal-to-noise ratio (SNR) gain performance of existing state-of-the-art probabilistic and TDOA-based speech separation algorithms by over 10 dB. This significant performance improvement is obtained by combining the information utilized by these approaches intelligently under a representative probabilistic description of the speech production and mixing process. The method is capable of recovering high fidelity estimates of the underlying speech sources even when there are more sources than microphone observations.

关键词： approximate inference microphone arrays phase-based speech processing probabilistic graphical models robust speech recognition speech separation variational methods

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Sensitivity analysis beyond linearity

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2019年 113卷 106-118页

作者： Leonelli, Manuele Univ Glasgow Sch Math & Stat Glasgow Lanark Scotland

A wide array of graphical models can be parametrized to have atomic probabilities represented by monomial functions. Such a monomial structure has proven very useful when studying robustness under the assumption of a multilinear model where all monomials have either zero or one exponents. Robustness in probabilistic graphical models is usually investigated by varying some of the input probabilities and observing the effects of these on output probabilities of interest. Here the assumption of multilinearity is relaxed and a general approach for one-way sensitivity analysis in non-multilinear models is presented. It is shown that in non-multilinear models sensitivity functions have a polynomial form, conversely to multilinear models where these are simply linear. The form of various divergences and distances under different covariation schemes is also formally derived. Proportional covariation is proven to be optimal in non-multilinear models under some specific choices of varied parameters. The methodology is illustrated throughout by an educational application. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Covariation Monomial models probabilistic graphical models Sensitivity analysis Staged trees

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A comprehensive review of conditional random fields: variants, hybrids and applications

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ARTIFICIAL INTELLIGENCE REVIEW 2020年第6期53卷 4289-4333页

作者： Yu, Bengong Fan, Zhaodi Hefei Univ Technol Sch Management Hefei Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei Anhui Peoples R China

The conditional random fields (CRFs) model plays an important role in the machine learning field. Driven by the development of the artificial intelligence, the CRF models have enjoyed great advancement. To analyze the recent development of the CRFs, this paper presents a comprehensive review of different versions of the CRF models and their applications. On the basis of elaborating on the background and definition of the CRFs, it analyzes three basic problems faced by the CRF models and reviews their latest improvements. Based on that, it presents the applications of the CRFs in the natural language processing, computer vision, biomedicine, Internet intelligence and other relevant fields. At last, specific analysis and future directions of the CRFs are discussed.

关键词： Conditional random fields probabilistic graphical models Machine learning Artificial intelligence

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Latent tree models for hierarchical topic detection

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ARTIFICIAL INTELLIGENCE 2017年 250卷 105-124页

作者： Chen, Peixian Zhang, Nevin L. Liu, Tengfei Poon, Leonard K. M. Chen, Zhourong Khawar, Farhan Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China Ant Financial Serv Grp Shanghai Peoples R China Educ Univ Hong Kong Dept Math & Informat Technol Hong Kong Hong Kong Peoples R China

We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTM5). The variables at the bottom level of an HLTM are observed binary variables that represent the presence/absence of words in a document. The variables at other levels are binary latent variables that represent word co-occurrence patterns or co-occurrences of such patterns. Each latent variable gives a soft partition of the documents, and document clusters in the partitions are interpreted as topics. Latent variables at high levels of the hierarchy capture long-range word co-occurrence patterns and hence give thematically more general topics, while those at low levels of the hierarchy capture short-range word co-occurrence patterns and give thematically more specific topics. In comparison with LDA-based methods, a key advantage of the new method is that it represents co-occurrence patterns explicitly using model structures. Extensive empirical results show that the new method significantly outperforms the LDA-based methods in term of model quality and meaningfulness of topics and topic hierarchies. (C) 2017 Elsevier B.V. All rights reserved.

关键词： probabilistic graphical models Text analysis Hierarchical latent tree analysis Hierarchical topic detection

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Spike and slab biclustering

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PATTERN RECOGNITION 2017年 72卷 186-195页

作者： Denitto, M. Bicego, M. Farinelli, A. Figueiredo, M. A. T. Univ Verona Str Le Grazie 15Ca Vignal 2 Verona Italy Univ Lisbon Inst Telecomunicacoes Ave Rovisco Pais 1 Lisbon Portugal Univ Lisbon Inst Super Tecn Ave Rovisco Pais 1 Lisbon Portugal

Biclustering refers to the problem of simultaneously clustering the rows and columns of a given data matrix, with the goal of obtaining submatrices where the selected rows present a coherent behaviour in the selected columns, and vice-versa. To face this intrinsically difficult problem, we propose a novel generative model, where biclustering is approached from a sparse low-rank matrix factorization perspective. The main idea is to design a probabilistic model describing the factorization of a given data matrix in two other matrices, from which information about rows and columns belonging to the sought for biclusters can be obtained. One crucial ingredient in the proposed model is the use of a spike and slab sparsity inducing prior, thus we term the approach spike and slab biclustering (SSBi). To estimate the parameters of the SSBi model, we propose an expectation-maximization (EM) algorithm, termed SSBiEM, which solves a low-rank factorization problem at each iteration, using a recently proposed augmented Lagrangian algorithm. Experiments with both synthetic and real data show that the SSBi approach compares favorably with the state-of-the-art. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Biclustering Spike and slab probabilistic graphical models Expectation-maximization

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A probabilistic Model for Component-Based Shape Synthesis

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ACM TRANSACTIONS ON GRAPHICS 2012年第4期31卷 1-11页

作者： Kalogerakis, Evangelos Chaudhuri, Siddhartha Koller, Daphne Koltun, Vladlen Stanford Univ Stanford CA 94305 USA

We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new generative model of component-based shape structure. The model represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation that can be effectively learned without supervision from a set of compatibly segmented shapes. We evaluate the model on a number of shape datasets with complex structural variability and demonstrate its application to amplification of shape databases and to interactive shape synthesis.

关键词： shape synthesis shape structure probabilistic graphical models machine learning data-driven 3D modeling

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Tagging Items Automatically Based on Both Content Information and Browsing Behaviors

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INFORMS JOURNAL ON COMPUTING 2021年第3期33卷 882-897页

作者： Liu, Shen Liu, Hongyan Tsinghua Univ Key Res Inst Humanities & Social Sci Univ Res Ctr Contemporary Management Sch Econ & Management Beijing 100084 Peoples R China

Tags have been adopted by many online services as a method to manage their online resources. Effective tagging benefits both users and firms. In real applications providing a user tagging mechanism, only a small portion of tags are usually provided by users. Therefore, an automatic tagging method, which can assign tags to different items automatically, is urgently needed. Previous works on automatic tagging focus on exploring the tagging behavior of users or the content information of items. In online service platforms, users frequently browse items related to their interests, which implies users' judgment about the underlying features of items and is helpful for automatic tagging. Browsing-behavior records are much more plentiful compared with tagging behavior and easy to collect. However, existing studies about automatic tagging ignore this kind of information. To properly integrate both browsing behaviors and content information for automatic tagging, we propose a novel probabilistic graphical model and develop a new algorithm for the model parameter inference. We conduct thorough experiments on a realworld data set to evaluate and analyze the performance of our proposed method. The experimental results demonstrate that our approach achieves better performance than state-of-the-art automatic tagging methods. Summary of Contribution. In this paper, we study how to automatically assign tags to items in an e-commerce background. Our study is about how to perform item tagging for e-commerce and other online service providers so that consumers can easily find what they need and firms can manage their resources effectively. Specifically, we study if consumer browsing behavior can be utilized to perform the tagging task automatically, which can save efforts of both firms and consumers. Additionally, we transform the problem into how to find the most proper tags for items and propose a novel probabilistic graphical model to model the generation process of tags. Finally,

关键词： automatic tagging exploratory consumer behavior probabilistic graphical models variational inference

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Learning and inferring motion patterns using parametric segmental Switching Linear Dynamic Systems

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2008年第1-3期77卷 103-124页

作者： Oh, Sang Min Rehg, James M. Balch, Tucker Dellaert, Frank Georgia Inst Technol Coll Comp GVU Ctr Atlanta GA 30332 USA

Switching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dynamic systems. An SLDS can describe complex temporal patterns more concisely and accurately than an HMM by using continuous hidden states. However, the use of SLDS models in practical applications is challenging for three reasons. First, exact inference in SLDS models is computationally intractable. Second, the geometric duration model induced in standard SLDSs limits their representational power. Third, standard SLDSs do not provide a principled way to interpret systematic variations governed by higher order parameters. The contributions in this paper address all of these three challenges. First, we present a data-driven MCMC (DD-MCMC) sampling method for approximate inference in SLDSs. We show DD-MCMC provides an efficient method for estimation and learning in SLDS models. Second, we present segmental SLDSs (S-SLDS), where the geometric distributions of the switching state durations are replaced with arbitrary duration models. Third, we extend the standard SLDS model with additional global parameters that can capture systematic temporal and spatial variations. The resulting parametric SLDS model (P-SLDS) uses EM to robustly interpret parametrized motions by incorporating additional global parameters that underly systematic variations of the overall motion. The overall development of the extensions for SLDSs provide a principled framework to interpret complex motions. The framework is applied to the honey bee dance interpretation task in the context of the on-going BioTracking project at the Georgia Institute of Technology. The experimental results suggest that the enhanced models provide an effective framework for a wide range of motion analysis applications.

关键词： probabilistic graphical models time-series trajectory analysis behavior recognition MCMC biology

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Statistical Association Mapping of Population-Structured Genetic Data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019年第2期16卷 638-649页

作者： Najafi, Amir Janghorbani, Sepehr Motahari, Seyed Abolfazl Fatemizadeh, Emad Sharif Univ Technol Dept Comp Engn Tehran *** Iran Sharif Univ Technol Dept Elect Engn Tehran *** Iran

Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the associated sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease-associated factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability distribution of the model parameters. We have implemented our proposed framework on a software package whose performance is extensively evaluated on a number of synthetic datasets, and compared to some of the well-known existing methods such as STRUCTURE. It has been shown that in extreme scenarios, up to 10 - 15 percent of improvement in the inference accuracy is achieved with a moderate increase in computational complexity.

关键词： Bioinformatics genome-wide association study probabilistic graphical models MCMC

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Scaling Up Markov Logic probabilistic Inference for Social Graphs

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2017年第2期29卷 433-445页

作者： Chen, Haiquan Ku, Wei-Shinn Wang, Haixun Tang, Liang Sun, Min-Te Valdosta State Univ Dept Comp Sci Valdosta GA 31698 USA Auburn Univ Dept Comp Sci & Software Engn Auburn AL 36849 USA Google Res 1600 Amphitheater Pkwy Mountain View CA 94043 USA Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan 32001 Taiwan

Link prediction is a fundamental problem in social network analysis. Although the link prediction problem is not new, the challenge of how to exploit various existing network information, such as network structure data and node attribute data, to enable AI-style knowledge inference for large social networks still remains unsolved. In this paper, we design and implement a scalable framework that treats link prediction as knowledge reasoning using Markov Logic Networks(MLNs). Differing from other probabilistic graphical models, MLNs allow undirected relationships with cycles and long-range (non-adjacent) dependency, which are essential and abound in social networks. In our framework, the prior knowledge is captured as the structure dependency (such as friendship) and the attribute dependency (such as social communities) in terms of inference rules, associated with uncertainty represented as probabilities. Next, we employ the random walk to discover the inference subgraph, on which probabilistic inference is performed, so that the required computation and storage cost can be significantly reduced without much sacrifice of the inference accuracy. Our extensive experiments with real-world datasets verify the superiority of our proposed approaches over two baseline methods and show that our approaches are able to provide a tunable tradeoff between inference accuracy and efficiency.

关键词： Social network analysis graph pruning probabilistic graphical models Markov logic network

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