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Single-Channel Speech Separation Based on non-negative matrix factorization and Factorial Conditional Random Field

Chinese Journal of Electronics 2018年第5期27卷 1063-1070页

作者： LI Xu TU Ming WANG Xiaofei WU Chao FU Qiang YAN Yonghong Key Laboratory of Speech Acoustics and Content Understanding Institute of AcousticsChinese Academy of Sciences Signal Analysis Representation and Perception Laboratory Arizona State University Xinjiang Laboratory of Minority Speech and Language Information Processing

A new non-negative matrix factorization(NMF) based algorithm is proposed for single-channel speech separation with a prior known speakers, which aims to better model the spectral structure and temporal continuity of speech signal. First, NMF and k-means clustering are employed to obtain multiple small dictionaries as well as a state sequence that describes the temporal dynamics between these dictionaries for each ***, a Factorial conditional random field(FCRF) model is trained using the state sequences and dictionaries to jointly model the temporal continuity of two speakers' mixed signal for separation. Experiments show that the proposed algorithm outperforms the baselines with respect to all metrics, for example sparse NMF(+1.12 dB SDR, +2.37 dB SIR, +0.40 dB SAR, +0.2 MOS), nonnegative factorial hidden Markov model(+2.04 dB SDR,+4.26 dB SIR, +0.62 dB SAR, +1.0 MOS) and standard NMF(+2.8 dB SDR, +5.08 dB SIR, +1.06 dB SAR, +1.2 MOS).

关键词： Single-channel speech separation non-negative matrix factorization Factorial conditional random field k-means clustering

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Max-margin non-negative matrix factorization with flexible spatial constraints based on factor analysis

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Frontiers of Computer Science 2016年第2期10卷 302-316页

作者： Dakun LIU Xiaoyang TAN Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

non-negative matrix factorization （NMF） is a popular feature encoding method for image understanding due to its non-negative properties in representation, but the learnt basis images are not always local due to the lack of explicit constraints in its objective. Various algebraic or geometric local constraints are hence proposed to shape the behaviour of the original NMF. Such constraints are usually rigid in the sense that they have to be specified beforehand instead of learning from the data. In this paper, we propose a flexible spatial constraint method for NMF learning based on factor analysis. Particularly, to learn the local spatial structure of the images, we apply a series of transformations such as orthogonal rotation and thresholding to the factor loading matrix obtained through factor analysis. Then we map the transformed loading matrix into a Laplacian matrix and incorporate this into a max-margin non-negative matrix factorization framework as a penalty term, aiming to learn a representation space which is non-negative, discriminative and localstructure-preserving. We verify the feasibility and effectiveness of the proposed method on several real world datasets with encouraging results.

关键词： non-negative matrix factorization factor analysis loading matrix flexible spatial constraints

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Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and non-negative matrix factorization

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Chinese Journal of Mechanical Engineering 2015年第1期28卷 96-105页

作者： GAO Huizhong LIANG Lin CHEN Xiaoguang XU Guanghua School of Mechanical Engineering Xi'an Jiaotong University The 705 Research Institute China Shipbuilding Industry Corporation Key Laboratory for Modern Design and Rotor-Bearing System of Education Ministry Xi'an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.

关键词： time-frequency distribution non-negative matrix factorization rolling element bearing feature extraction

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TOPIC SPLITTING： A HIERARCHICAL TOPIC MODEL BASED ON non-negative matrix factorization

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Journal of Systems Science and Systems Engineering 2018年第4期27卷 479-496页

作者： Rui Liu Xingguang Wang Deqing Wang Yuan Zuo He Zhang Xianzhu Zheng SKLSDE School of Computer Science Beihang University Beijing 100191 China Department of Scientific Research Academy of Armored Force Engineering Beijing 100072 China

Hierarchical topic model has been widely applied in many real applications, because it can build a hierarchy on topics with guaranteeing of topics＇ quality. Most of traditional methods build a hierarchy by adopting low-level topics as new features to construct high-level ones, which will often cause semantic confusion between low-level topics and high-level ones. To address the above problem, we propose a novel topic model named hierarchical sparse NMF with orthogonal constraint （HSOC）, which is based on non-negative matrix factorization and builds topic hierarchy via splitting super-topics into sub-topics. In HSOC, we introduce global independence, local independence and information consistency to constraint the split topics. Extensive experimental results on real-world corpora show that the purposed model achieves comparable performance on topic quality and better performance on semantic feature representation of documents compared with baseline methods.

关键词： Hierarchical topic model non-negative matrix factorization hierarchical NMF topic splitting

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A New Adaptive Robust Modularized Semi-Supervised Community Detection Method Based on non-negative matrix factorization

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NEURAL PROCESSING LETTERS 2024年第2期56卷 134-134页

作者： Ghadirian, Mohammad Bigdeli, Nooshin Imam Khomeini Int Univ Fac Tech & Engn Dept Control Engn Qazvin Iran

The most extensively used tools for categorizing complicated networks are community detection methods. One of the most common methods for unsupervised and semi-supervised clustering is community detection based on non-negative matrix factorization (NMF). nonetheless, this approach encounters multiple challenges, including the lack of specificity for the data type and the decreased efficiency when errors occur in each cluster's knowledge priority. As modularity is the basic and thorough criterion for evaluating and validating performance of community detection methods, this paper proposes a new approach for modularity-based community detection which is similar to symmetric NMF. The provided approach is a semi-supervised adaptive robust community detection model referred to as modularized robust semi-supervised adaptive symmetric NMF (MRASNMF). In this model, the modularity criterion has been successfully combined with the NMF model via a novel multi-view clustering method. Also, the tuning parameter is adjusted iteratively via an adaptive method. MRASNMF makes use of knowledge priority, modularity criterion, reinforcement of non-negative matrix factorization, and has iterative solution, as well. In this regard, the MRASNMF model was evaluated and validated using five real-world networks in comparison to existing semi-supervised community detection approaches. According to the findings of this study, the proposed strategy is most effective for all types of networks.

关键词： non-negative matrix factorization Community detection Semi-supervised clustering Robust clustering Multi-view community detection

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PSI: Patch-based script identification using non-negative matrix factorization

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PATTERN RECOGNITION 2017年 67卷 328-339页

作者： Arabnejad, Ehsan Moghaddam, Reza Farrahi Cheriet, Mohamed Ecole Technol Super Synchromedia Lab Multimedia Commun Telepresence Montreal PQ H3C 1K3 Canada

Script identification is an important step in automatic understanding of ancient manuscripts because there is no universal script-independent understanding tool available. Unlike the machine-printed and modern documents, ancient manuscripts are highly-unconstrained in structure and layout and suffer from various types of degradation and noise. These challenges make automatic script identification of ancient manuscripts a difficult task. In this paper, a novel method for script identification of ancient manuscripts is proposed which uses a representation of images by a set of overlapping patches, and considers the patches as the lowest unit of representation (objects). non-negative matrix factorization (NMF), motivated by the structure of the patches and the non-negative nature of images, is used as feature extraction method to create low-dimensional representation for the patches and also to learn a dictionary. This dictionary will be used to project all of the patches to a low-dimensional space. A second dictionary is learned using the K-means algorithm for the purpose of speeding up the algorithm. These two dictionaries are used for classification of new data. The proposed method is robust with respect to degradation and needs less normalization. The performance and reliability of the proposed method have been evaluated against state-of-the-art methods on an ancient manuscripts dataset with promising results. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Script identification non-negative matrix factorization Patch representation Clustering

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Learning sparse representations by non-negative matrix factorization and sequential cone programming

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JOURNAL OF MACHINE LEARNING RESEARCH 2006年第7期7卷 1385-1407页

作者： Heiler, Matthias Schnoerr, Christoph Univ Mannheim Dept Math & Comp Sci Comp Vis Graph & Pattern Recognit Grp D-68131 Mannheim Germany

We exploit the biconvex nature of the Euclidean non-negative matrix factorization (NMF) optimization problem to derive optimization schemes based on sequential quadratic and second order cone programming. We show that for ordinary NMF, our approach performs as well as existing state-of-the-art algorithms, while for sparsity-constrained NMF, as recently proposed by P.O. Hoyer in JMLR 5 (2004), it outperforms previous methods. In addition, we show how to extend NMF learning within the same optimization framework in order to make use of class membership information in supervised learning problems.

关键词： non-negative matrix factorization second-order cone programming sequential convex optimization reverse-convex programming sparsity

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Combining a recursive approach via non-negative matrix factorization and Gini index sparsity to improve reliable detection of wheezing sounds

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 147卷 113212-113212页

作者： De La Torre Cruz, Juan Canadas Quesada, Francisco Jesus Carabias Orti, Julio Jose Vera Candeas, Pedro Ruiz Reyes, Nicolas Univ Jaen Dept Telecommun Engn Campus Cient Tecnol LinaresAvda Univ S-N Jaen 23700 Spain

Auscultation constitutes a fast, non-invasive and low-cost tool widely used to diagnose respiratory diseases in most of the health centres. However, the acoustic training and expertise acquired by the physician is still crucial to provide a reliable diagnosis of the status of the lung. Each wrong diagnosis increases the risk to the health of patients and the costs associated with the treatment of the disease detected. A wheezing detection system can be useful to the physician to minimize the subjectivity of the interpretation of the breathing sounds, misdiagnoses due to stress and elucidating complex acoustic scenes (such as louder background noises). Highlight that the presence of wheeze sounds is one of the main indicators of respiratory disorders from airway obstructions. This work presents an expert and intelligent system to detect wheeze sounds based on a recursive algorithm that combines orthogonal non-negative matrix factorization (ONMF) and the sparsity descriptor Gini index. The recursive algorithm is composed of four stages. The first stage is based on ONMF modelling to factorize the spectral bases as dissimilar as possible. The second stage clusters the ONMF bases into two categories: wheezing and normal breath. The third stage proposes a novel stopping criterion that controls the loss of wheezing spectral content at the expense of removing normal breath content in the recursive algorithm. Finally, the fourth stage determines the patient's condition to locate the temporal intervals in which wheeze sounds are active for unhealthy patients. Experimental results report that the proposed method: (i) provides the best detection performance compared to the recent state-of-the-art wheezing detection approaches, achieving the highest robustness in noisy environments;and (ii) reliably distinguishes the patient's condition (healthy/unhealthy). The strengths of the proposed method are the following: (i) its unsupervised nature since it does not depend on any trainin

关键词： Wheezing Detection non-negative matrix factorization Gini index Sparsity Clustering

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Linear Frequency Modulated Reverberation Suppression Using non-negative matrix factorization Methods, Dechirping Transformation and Modulo Operation

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IEEE ACCESS 2020年 8卷 110720-110737页

作者： Kim, Geunhwan Lee, Kyunkyung Lee, Seokjin Kyungpook Natl Univ Sch Elect Engn Coll IT Engn Daegu 41566 South Korea

In this study, we propose a reverberation suppression algorithm for linear frequency-modulated (LFM) pulse sonar systems using a non-negative matrix factorization (NMF) method. Because conventional NMF-based reverberation suppression algorithms are only applicable to continuous wave reverberation, we propose two pre-processing methods, namely dechirping transformation and modulo operation, to facilitate application of the NMF method to LFM reverberation. Moreover, we impose additional sparse constraints on the NMF method to improve its performance. To evaluate the proposed algorithm, an experiment involving simulated LFM reverberation is performed. The results thereof show improved detection performance at several signal-to-reverberation ratios and false alarm conditions. Moreover, the proposed algorithm is also applied to sea experiment data. According to the sea experiment analysis, the algorithm is able to suppress the LFM reverberation effectively and improve detection performance in practical LFM pulse sonar systems.

关键词： Reverberation Sonar Time-frequency analysis Spectrogram Doppler effect matrix decomposition Sparse matrices Active sonar dechirping transformation modulo operation non-negative matrix factorization reverberation suppression sparse constraints

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Reconstruction of Raman Spectra of Biochemical Mixtures Using Group and Basis Restricted non-negative matrix factorization

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APPLIED SPECTROSCOPY 2023年第7期77卷 698-709页

作者： Milligan, Kirsty Scarrott, Kendra Andrews, Jeffrey L. Brolo, Alexandre G. Lum, Julian J. Jirasek, Andrew Univ British Columbia Okanagan Dept Phys Kelowna BC Canada Univ British Columbia Okanagan Fac Med Southern Med Program Kelowna BC Canada Univ British Columbia Okanagan Dept Stat Kelowna BC Canada Univ Victoria Dept Chem Victoria BC Canada Univ Victoria Dept Biochem & Microbiol Victoria BC Canada Univ British Columbia Okanagan Dept Phys ASC 354-3187 Univ Way Kelowna BC V1V 1V7 Canada

Raman spectroscopy is a useful tool for obtaining biochemical information from biological samples. However, interpretation of Raman spectroscopy data in order to draw meaningful conclusions related to the biochemical make up of cells and tissues is often difficult and could be misleading if care is not taken in the deconstruction of the spectral data. Our group has previously demonstrated the implementation of a group- and basis-restricted non-negative matrix factorization (GBR-NMF) framework as an alternative to more widely used dimensionality reduction techniques such as principal component analysis (PCA) for the deconstruction of Raman spectroscopy data as related to radiation response monitoring in both cellular and tissue data. While this method provides better biological interpretability of the Raman spectroscopy data, there are some important factors which must be considered in order to provide the most robust GBR-NMF model. We here evaluate and compare the accuracy of a GBR-NMF model in the reconstruction of three mixture solutions of known concentrations. The factors assessed include the effect of solid versus solutions bases spectra, the number of unconstrained components used in the model, the tolerance of different signal to noise thresholds, and how different groups of biochemicals compare to each other. The robustness of the model was assessed by how well the relative concentration of each individual biochemical in the solution mixture is reflected in the GBR-NMF scores obtained. We also evaluated how well the model can reconstruct original data, both with and without the inclusion of an unconstrained component. Overall, we found that solid bases spectra were generally comparable to solution bases spectra in the GBR-NMF model for all groups of biochemicals. The model was found to be relatively tolerant of high levels of noise in the mixture solutions using solid bases spectra. Additionally, the inclusion of an unconstrained component did not have a sig

关键词： Raman spectroscopy non-negative matrix factorization NMF group and basis-restricted non-negative matrix factorization GBR-NMF radiation therapy radiation response

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