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Binaural Localization of Multiple Sound Sources by non-negative tensor factorization

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2018年第6期26卷 1068-1078页

作者： Benaroya, Elie Laurent Obin, Nicolas Liuni, Marco Roebel, Axel Raumel, Wilson Argentieri, Sylvain Sorbonne Univ CNRS IRCAM F-75005 Paris France Sorbonne Univ CNRS IRCAM STMS F-75005 Paris France Sorbonne Univ CNRS ISIR F-75005 Paris France

This paper presents non-negative factorization of audio signals for the binaural localization of multiple sound sources within realistic and unknown sound environments. non-negative tensor factorization (NTF) provides a sparse representation of multichannel audio signals in time, frequency, and space that can be exploited in computational audio scene analysis and robot audition for the separation and localization of sound sources. In the proposed formulation, each sound source is represented by means of spectral dictionaries, temporal activation, and its distribution within each channel (here, left and right ears). This distribution, being dependent on the frequency, can be interpreted as an explicit estimation of the Head-Related Transfer Function (HRTF) of a binaural head which can then be converted into the estimated sound source position. Moreover, the semisupervised formulation of the non-negative factorization allows us to integrate prior knowledge about some sound sources of interest whose dictionaries can be learned in advance, whereas the remaining sources are considered as background sound, which remains unknown and is estimated on the fly. The proposed NTF-based sound source localization is applied here to binaural sound source localization of multiple speakers within realistic sound environments.

关键词： Binaural localization robot audition computational audio scene analysis non-negative tensor factorization

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Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing

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JOURNAL OF COMPUTATIONAL PHYSICS 2019年 395卷 85-104页

作者： Vesselinov, V. V. Mudunuru, M. K. Karra, S. O'Malley, D. Alexandrov, B. S. Los Alamos Natl Lab Earth & Environm Sci Div Computat Earth Sci Grp Los Alamos NM 87545 USA Univ Maryland Dept Comp Sci & Elect Engn Baltimore MD 21250 USA Los Alamos Natl Lab Theoret Div Phys & Chem Mat Grp Los Alamos NM 87545 USA

Analysis of reactive-diffusion simulations representing complex mixing processes requires a large number of independent model runs. For each high-fidelity model simulation, the model inputs are varied and the predicted mixing behavior is represented by temporal and spatial changes in species concentration. It is then required to discern how the model inputs (such as diffusivity, dispersion, anisotropy, and velocity field properties) impact the mixing process. This task is challenging and typically involves interpretation of large model outputs representing temporal and spatial changes of species concentration within the model domain. However, the task can be automated and substantially simplified by applying Machine Learning (ML) methods. In this paper, we present an application of an unsupervised ML method (called NTFk) using non-negative tensor factorization (NTF) coupled with a custom clustering procedure based on k-means to reveal the temporal and spatial features in product concentrations. An attractive and unique aspect of the proposed ML method is that it ensures the extracted features are non-negative, which is important to obtain a meaningful deconstruction of the mixing processes. The ML methodology is applied to a large set of high-resolution finite-element model simulations representing anisotropic reaction-diffusion processes in perturbed vortex-based velocity fields. The applied finite-element method ensures that spatial and temporal species concentration are always non-negative, even in the case of high anisotropic contrasts. The simulated reaction is a fast irreversible bimolecular reaction A + B -> C, where species A and B react to form species C. The reactive-diffusion model input parameters that control mixing include properties of the velocity field (such as vortex structures), anisotropic dispersion, and molecular diffusion. We demonstrate the applicability of the ML feature extraction method to produce a meaningful deconstruction of model outpu

关键词： non-negative tensor factorization Unsupervised machine learning Structure-preserving feature extraction Reactive-mixing Anisotropic dispersion non-negative solutions

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non-negative tensor factorization for vibration-based local damage detection

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年第1期198卷

作者： Gabor, Mateusz Zdunek, Rafal Zimroz, Radoslaw Wodecki, Jacek Wylomanska, Agnieszka Wroclaw Univ Sci & Technol Fac Elect Photon & Microsyst Janiszewskiego 11 PL-50372 Wroclaw Poland Wroclaw Univ Sci & Technol Fac Geoengn Min & Geol Grobli 15 PL-50421 Wroclaw Poland Wroclaw Univ Sci & Technol Fac Pure & Appl Math Hugo Steinhaus Ctr Hoene Wronskiego 13c PL-50376 Wroclaw Poland

In this study, a novel non-negative tensor factorization (NTF)-based method for vibration -based local damage detection in rolling element bearings is proposed. As the diagnostic signal registered from a faulty machine is non-stationary, the time-frequency method is frequently used as a primary decomposition technique. It is proposed here to extract multi-linear NTF-based components from a 3D array of time-frequency representations of an observed signal partitioned into blocks. As a result, frequency and temporal informative components can be efficiently separated from non-informative ones. The experiments performed on synthetic and real signals demonstrate the high efficiency of the proposed method with respect to the already known non-negative matrix factorization approach.

关键词： Fault detection Bearings Vibration non-negative matrix factorization non-negative tensor factorization

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On Ambisonic Source Separation With Spatially Informed non-negative tensor factorization

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2024年 32卷 3238-3255页

作者： Guzik, Mateusz Kowalczyk, Konrad AGH Univ Krakow Inst Elect Fac Comp Sci Elect & Telecommun Signal Proc Grp PL-30059 Krakow Poland

This article presents a non-negative tensor factorization based method for sound source separation from Ambisonic microphone signals. The proposed method enables the use of prior knowledge about the Directions-of-Arrival (DOAs) of the sources, incorporated through a constraint on the Spatial Covariance Matrix (SCM) within a Maximum a Posteriori (MAP) framework. Specifically, this article presents a detailed derivation of four algorithms that are based on two types of cost functions, namely the squared Euclidean distance and the Itakura-Saito divergence, which are then combined with two prior probability distributions on the SCM, that is the Wishart and the Inverse Wishart. The experimental evaluation of the baseline Maximum Likelihood (ML) and the proposed MAP methods is primarily based on first-order Ambisonic recordings, using four different source signal datasets, three with musical pieces and one containing speech utterances. We consider under-determined, determined, as well as over-determined scenarios by separating two, four and six sound sources, respectively. Furthermore, we evaluate the proposed algorithms for different spherical harmonic orders and at different reverberation time levels, as well as in non-ideal prior knowledge conditions, for increasingly more corrupted DOAs. Overall, in comparison with beamforming and a state-of-the-art separation technique, as well as the baseline ML methods, the proposed MAP approach offers superior separation performance in a variety of scenarios, as shown by the analysis of the experimental evaluation results, in terms of the standard objective separation measures, such as the SDR, ISR, SIR and SAR.

关键词： Source separation non-negative tensor factorization ambisonics spherical harmonics localization prior

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Analysis of trends in seasonal electrical energy consumption via non-negative tensor factorization

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NEUROCOMPUTING 2015年 170卷 318-327页

作者： Figueiredo, Marisa Ribeiro, Bernardete de Almeida, Ana Univ Coimbra Dept Informat Engn CISUC Polo 2 P-3030290 Coimbra Portugal Univ Coimbra Dept Informat Engn CISUC Polo 2 P-3030290 Coimbra Portugal Inst Univ Lisboa ISCTE Univ Ctr Lisbon P-1649026 Lisbon Portugal

This paper looks at the extraction of trends of household electrical seasonal consumption via load disaggregation. With the proviso that data for several home devices can be embedded in a tensor, non-negative multi-way array factorization is performed in order to extract the most relevant components. In the initial decomposition step the decomposed signals are incorporated in the test signal consisting of the whole-home measured consumption. After this the disaggregated data corresponding to each electrical device is obtained by factorizing the associated matrix through the learned model. Finally, we evaluate the performance of load disaggregation by the supervised method, and study the trends along several years and across seasons. Towards this end, computational experiments were yielded using real-world data from household electrical consumption measurements along several years. While breaking down the whole house energy consumption into appliance level gives less accurate estimates in the late years, we empirically show the adequacy of this method for handling the earlier years and the estimates of the underlying seasonal trend-cycle. (C) 2015 Elsevier B.V. All rights reserved.

关键词： non-negative tensor factorization Electrical signal disaggregation non-intrusive load monitoring (NILM) Energy efficiency

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Application of non-negative tensor factorization for Airport Flight Delay Pattern Recognition

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IEEE ACCESS 2019年 7卷 171724-171737页

作者： Zhong, Han Qi, Geqi Guan, Wei Hua, Xiaochen Beijing Jiaotong Univ MOT Key Lab Transport Ind Big Data Applicat Techn Beijing 100044 Peoples R China Civil Aviat Univ China Coll Air Traff Management Tianjin 300300 Peoples R China Tianjin Subbur Air Traff Management Tianjin 300000 Peoples R China

With the rapid development of civil aviation transportation in China, huge demand growth has broken the balance between supply and demand, resulting in airspace congestion and increasing flight delays. The delays of large airports have been increasing year by year, which has seriously affected the air travel experience of passengers. Obtaining their flight delay patterns can help identify defects in flight scheduling and airspace utilization. The investigation based on the actual flight operation data of Tianjin Binhai International Airport (TSN) is conducted, in order to capture the relationship and impact between the factors such as traffic flow direction, airline attributes and hourly average delay distribution. Furthermore, non-negative tensor factorization (NTF) is applied to pattern recognition by introducing CP (CANDECOMP/PARAFAC) decomposition and Block Coordinate Descent (BCD) algorithm for selected data set. Numerical experiments show that the designed method has good performance in terms of computation speed and solution quality. Recognition results indicate the significant pattern characteristics of the Tianjin airport delay are extracted, which can provide some new perspectives for air traffic management unit to alleviate airspace congestion and improve service quality.

关键词： Air traffic flight delay non-negative tensor factorization pattern recognition

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Analysis of Large-Scale Traffic Dynamics in an Urban Transportation Network Using non-negative tensor factorization

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INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH 2016年第1期14卷 36-49页

作者： Han, Yufei Moutarde, Fabien INRIA RITS Domaine Voluceau F-78150 Rocquencourt France PSL Res Univ Mines ParisTech Robot Lab CAOR 60 Blvd St Michel F-75006 Paris France

In this paper, we present our work on clustering and prediction of temporal evolution of global congestion configurations in a large-scale urban transportation network. Instead of looking into temporal variations of traffic flow states of individual links, we focus on temporal evolution of the complete spatial configuration of congestions over the network. In our work, we pursue to describe the typical temporal patterns of the global traffic states and achieve long-term prediction of the large-scale traffic evolution in a unified data-mining framework. To this end, we formulate this joint task using regularized non-negative tensor factorization, which has been shown to be a useful analysis tool for spatio-temporal data sequences. Clustering and prediction are performed based on the compact tensor factorization results. The validity of the proposed spatio-temporal traffic data analysis method is shown on experiments using simulated realistic traffic data.

关键词： Large-scale traffic dynamics non-negative tensor factorization

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Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2015年第5期22卷 1009-1019页

作者： Luo, Yuan Xin, Yu Hochberg, Ephraim Joshi, Rohit Uzuner, Ozlem Szolovits, Peter MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA Harvard Univ Sch Med Massachusetts Gen Hosp Ctr Lymphoma Cambridge MA 02138 USA Harvard Univ Sch Med Dept Med Cambridge MA 02138 USA SUNY Albany Dept Informat Studies Albany NY 12222 USA

Objective Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability. Methods The authors introduce a novel framework named subgraph augmented non-negative tensor factorization (SANTF). In addition to relying on atomic features (e.g., words in clinical narrative text), SANTF automatically mines higher-order features (e.g., relations of lymphoid cells expressing antigens) from clinical narrative text by converting sentences into a graph representation and identifying important subgraphs. The authors compose a tensor using patients, higher-order features, and atomic features as its respective modes. We then apply non-negative tensor factorization to cluster patients, and simultaneously identify latent groups of higher-order features that link to patient clusters, as in clinical guidelines where a panel of immunophenotypic features and laboratory results are used to specify diagnostic criteria. Results and Conclusion SANTF demonstrated over 10% improvement in averaged F-measure on patient clustering compared to widely used non-negative matrix factorization (NMF) and k-means clustering methods. Multiple baselines were established by modeling patient data using patient-by-features matrices with different feature configurations and then performing NMF or k-means to cluster patients. Feature analysis identified latent groups of higher-order features that lead to medical insights. We also found that the latent groups of atomic features help to better correlate the latent groups of higher-order features.

关键词： non-negative tensor factorization unsupervised learning subgraph mining natural language processing

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General-purpose Unsupervised Cyber Anomaly Detection via non-negative tensor factorization

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DIGITAL THREATS: RESEARCH AND PRACTICE 2023年第1期4卷 1-28页

作者： Eren, Maksim E. Moore, Juston S. Skau, Erik Moore, Elisabeth Bhattarai, Manish Chennupati, Gopinath Alexandrov, Boian S. Los Alamos Natl Lab Adv Res Cyber Syst Los Alamos NM 87545 USA Los Alamos Natl Lab Informat Sci Los Alamos NM USA Los Alamos Natl Lab Div Theoret Los Alamos NM USA Alexa Amazon Bellevue WA USA

Distinguishing malicious anomalous activities from unusual but benign activities is a fundamental challenge for cyber defenders. Prior studies have shown that statistical user behavior analysis yields accurate detections by learning behavior profiles from observed user activity. These unsupervised models are able to generalize to unseen types of attacks by detecting deviations from normal behavior without knowledge of specific attack signatures. However, approaches proposed to date based on probabilistic matrix factorization are limited by the information conveyed in a two-dimensional space. non-negative tensor factorization, however, is a powerful unsupervised machine learning method that naturally models multi-dimensional data, capturing complex and multi-faceted details of behavior profiles. Our new unsupervised statistical anomaly detection methodology matches or surpasses state-of-the-art supervised learning baselines across several challenging and diverse cyber application areas, including detection of compromised user credentials, botnets, spam e-mails, and fraudulent credit card transactions.

关键词： Anomaly detection Poisson tensor factorization non-negative tensor factorization unsupervised learning cyber security CPD malware data fusion ensemble learning GPU

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non-negative tensor factorization for Human Behavioral Pattern Mining in Online Games

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INFORMATION 2018年第3期9卷 66页

作者： Sapienza, Anna Bessi, Alessandro Ferrara, Emilio USC Inst Informat Sci Marina Del Rey CA 90292 USA

Multiplayer online battle arena is a genre of online games that has become extremely popular. Due to their success, these games also drew the attention of our research community, because they provide a wealth of information about human online interactions and behaviors. A crucial problem is the extraction of activity patterns that characterize this type of data, in an interpretable way. Here, we leverage the non-negative tensor factorization to detect hidden correlated behaviors of playing in a well-known game: League of Legends. To this aim, we collect the entire gaming history of a group of about 1000 players, which accounts for roughly 100K matches. By applying our framework we are able to separate players into different groups. We show that each group exhibits similar features and playing strategies, as well as similar temporal trajectories, i.e., behavioral progressions over the course of their gaming history. We surprisingly discover that playing strategies are stable over time and we provide an explanation for this observation.

关键词： non-negative tensor factorization temporal and topological pattern mining human behavior multiplayer online game

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