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The Journal of Machine Learning Research 2017年第1期18卷

作者： Weiwei Liu Ivor W. Tsang Klaus-Robert Müller School of Computer Science and Engineering The University of New South Wales Sydney NSW Australia and Centre for Artificial Intelligence FEIT University of Technology Sydney NSW Australia Centre for Artificial Intelligence FEIT University of Technology Sydney NSW Australia Machine Learning Group Computer Science Berlin Institute of Technology Berlin Germany and Max Planck Institute for Informatics Saarbrcken and Department of Brain and Cognitive Engineering Korea University Seoul Korea

Many applications, such as human action recognition and object detection, can be formulated as a multiclass classification problem. One-vs-rest (OVR) is one of the most widely used approaches for multiclass classification due to its simplicity and excellent performance. However, many confusing classes in such applications will degrade its results. For example, hand clap and boxing are two confusing actions. Hand clap is easily misclassified as boxing, and vice versa. Therefore, precisely classifying confusing classes remains a challenging task. To obtain better performance for multiclass classifications that have confusing classes, we first develop a classifier chain model for multiclass classification (CCMC) to transfer class information between classifiers. Then, based on an analysis of our proposed model, we propose an easy-to-hard learning paradigm for multiclass classification to automatically identify easy and hard classes and then use the predictions from simpler classes to help solve harder classes. Similar to CCMC, the classifier chain (CC) model is also proposed by Read et al. (2009) to capture the label dependency for multi-label classification. However, CC does not consider the order of di_culty of the labels and achieves degenerated performance when there are many confusing labels. Therefore, it is non-trivial to learn the appropriate label order for CC. Motivated by our analysis for CCMC, we also propose the easy-to-hard learning paradigm for multi-label classification to automatically identify easy and hard labels, and then use the predictions from simpler labels to help solve harder labels. We also demonstrate that our proposed strategy can be successfully applied to a wide range of applications, such as ordinal classification and relationship prediction. Extensive empirical studies validate our analysis and the efiectiveness of our proposed easy-to-hard learning strategies.

关键词： classifier Chain easy-to-hard learning paradigm multi-label classification multiclass classification

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From brain volumes to subgroup classification in genetic mutation carriers for frontotemporal dementia: A cluster analysis in the GENFI study

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Alzheimer's & Dementia 2021年第S5期17卷

作者： Martina Bocchetta Emily G. Todd Jennifer M. Nicholas Carolin Heller Imogen J. Swift Georgia Peakman David M. Cash Rhian S. Convery Lucy L. Russell David L. Thomas Juan Eugenio Iglesias John C. van Swieten Lize C. Jiskoot Harro Seelaar Barbara Borroni Daniela Galimberti Raquel Sanchez-Valle Robert Laforce Jr. Fermin Moreno Matthis Synofzik Caroline Graff Mario Masellis Maria Carmela Tartaglia James B. Rowe Rik Vandenberghe Elizabeth Finger Fabrizio Tagliavini Alexandre Mendonca Isabel Santana Christopher Butler Simon Ducharme Alexander Gerhard Adrian Danek Johannes Levin Markus Otto Sandro Sorbi Isabelle Le Ber Florence Pasquier Jonathan D. Rohrer Genetic Frontotemporal dementia Initiative (GENFI) Dementia Research Centre UCL Queen Square Institute of Neurology University College London London United Kingdom Department of Medical Statistics London School of Hygiene and Tropical Medicine London United Kingdom Dementia Research Centre Queen Square Institute of Neurology University College London London United Kingdom Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Dementia Research Centre Department of Neurodegenerative Disease UCL Institute of Neurology Queen Square London United Kingdom Neuroradiological Academic Unit UCL Queen Square Institute of Neurology UCL London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical School Charlestown MA USA Alzheimer Center Erasmus Medical Center Rotterdam Netherlands Erasmus MC Rotterdam Netherlands Erasmus Medical Center Rotterdam Netherlands Centre for Neurodegenerative Disorders Neurology Unit Department of Clinical and Experimental Sciences University of Brescia Brescia Italy University of Milan Milan Italy Department of Biomedical Surgical and Dental Sciences University of Milan Milan Italy Neurology Department Hospital Clínic. Institut d'Investigacions Biomediques Barcelona Spain Clinique Interdisciplinaire de Mémoire CHU de Québec/Université Laval/Hôpital de l’Enfant-Jésus Quebec City QC Canada Hospital Universitario Donostia San Sebastian Spain Centre for Neurology and Hertie-Institute for Clinical Brain Research Hoppe-Seyler-Str Tuebingen Germany Karolinska Institutet Department NVS Division of Neurogeriatrics Karolinska Institutet Stockholm Sweden Unit for Hereditary Dementia Theme Aging Karolinska University Hospital-Solna Stockholm Sweden LC Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute Toronto ON Canada T

Background Genetic frontotemporal dementia (FTD) is highly heterogeneous, with carriers of mutations in the same gene manifesting different phenotypes. Using in vivo MR images from the Genetic FTD Initiative (GENFI), we aimed to identify subgroups within the same genetic group whose brains were affected differently. Method Cortical and subcortical volumes of interest were generated using automated parcellation methods on volumetric 3T T1-weighted MRI scans for 479 carriers (198 GRN , 202 C9orf72 , and 79 MAPT mutation carriers). W-scores for 85 volumes of interest were computed from a linear regression model carried out on 298 non-carrier cognitively normal controls adjusting for the effect of age, sex, total intracranial volume and scanner type. Cluster analyses with the Ward agglomerating method were performed on all w-scores while considering the three genetic groups independently. The identified clusters were then compared for age, estimated years from onset, global and sum of boxes scores of the CDR® plus NACC FTLD (at baseline and after one year), neurofilament light chain (NfL) levels in the plasma and w-scores in brain regions typically showing early atrophy (Kruskal-Wallis test). Result We identified three clusters among the GRN mutation carriers and four in the MAPT and C9orf72 groups, which were all significantly different for the variables reported in the Table (p-value<0.003). For all three genetic groups, one cluster was formed by patients with a clinical diagnosis of FTD, with more extensive atrophy and increased disease severity. For the remaining clusters, there seemed to be an association with disease severity for MAPT and GRN mutation carriers but not so for C9orf72 expansion carriers where clinical scores were not clearly associated with a specific cluster. Conclusion By only looking at regional brain volumes, we were able to detect different clusters within carriers of mutations in the same gene, with C9orf72 expansion carriers being the most he

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Detecting clinical progression from abnormal regional brain volumes at baseline in genetic frontotemporal dementia: A GENFI study

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Alzheimer's & Dementia 2021年第S1期17卷

作者： Martina Bocchetta Emily G Todd Jennifer M Nicholas Georgia Peakman David M Cash Rhian S Convery Lucy L Russell David L Thomas Juan Eugenio Iglesias John C van Swieten Lize C. Jiskoot Harro Seelaar Barbara Borroni Daniela Galimberti Raquel Sanchez-Valle Robert Laforce Jr. Fermin Moreno Matthis Synofzik Caroline Graff Mario Masellis Maria Carmela Tartaglia James B Rowe Rik Vandenberghe Elizabeth Finger Fabrizio Tagliavini Alexandre Mendonca Isabel Santana Christopher Butler Simon Ducharme Alexander Gerhard Adrian Danek Johannes Levin Markus Otto Sandro Sorbi Isabelle Le Ber Florence Pasquier Jonathan D Rohrer Genetic Frontotemporal dementia Initiative (GENFI) Dementia Research Centre UCL Queen Square Institute of Neurology University College London London United Kingdom Department of Medical Statistics London School of Hygiene and Tropical Medicine London United Kingdom Dementia Research Centre Department of Neurodegenerative Disease UCL Institute of Neurology Queen Square London United Kingdom Dementia Research Centre Queen Square Institute of Neurology University College London London United Kingdom Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Neuroradiological Academic Unit UCL Queen Square Institute of Neurology UCL London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical School Charlestown MA USA Alzheimer Center Erasmus Medical Center Rotterdam Netherlands Erasmus MC Rotterdam Netherlands Erasmus Medical Center Rotterdam Netherlands Centre for Neurodegenerative Disorders Neurology Unit Department of Clinical and Experimental Sciences University of Brescia Brescia Italy University of Milan Milan Italy Department of Biomedical Surgical and Dental Sciences University of Milan Milan Italy Neurology Department Hospital Clínic Institut d'Investigacions Biomediques Barcelona Spain Clinique Interdisciplinaire de Mémoire CHU de Québec/Université Laval/Hôpital de l’Enfant-Jésus Quebec City QC Canada Hospital Universitario Donostia San Sebastian Spain Centre for Neurology and Hertie-Institute for Clinical Brain Research Hoppe-Seyler-Str Tuebingen Germany Karolinska Institutet Department NVS Division of Neurogeriatrics Karolinska Institutet Stockholm Sweden Unit for Hereditary Dementia Theme Aging Karolinska University Hospital-Solna Stockholm Sweden LC Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute Toronto ON Canada T

Background Genetic frontotemporal dementia is highly heterogeneous, with different progression patterns seen between individuals. Using in vivo MR images from the Genetic FTD Initiative (GENFI), we aimed to identify clinical progression in genetic mutation carriers from their brain volumes at baseline. Method Cortical and subcortical volumes of interest (VOIs) were generated using automated parcellation methods on volumetric 3T T1-weighted MRI scans for 480 carriers (198 GRN , 202 C9orf72 , 80 MAPT ). W-scores for 79 VOIs were computed from a linear regression model carried out on 298 non-carrier cognitively normal controls adjusting for the effect of age, sex, total intracranial volume and scanner type. Carriers were divided into three disease stages based on their global CDR® plus NACC FTLD score (CDR-GS): asymptomatic (0), possibly or mildly symptomatic (0.5) and fully symptomatic (1 or more). Cut-off points for each VOI were derived from Youden indices estimated with ROC curves to distinguish between CDR-GS=0 and CDR-GS≥1 within each gene. CDR-GS=0.5 carriers (30 GRN , 32 C9orf72 , 13 MAPT ) were classified as ‘normal’ or ‘abnormal’ based on these cut-off points. We compared the CDR® plus NACC FTLD sum-of-boxes scores (CDR-SOB) at one year follow-up in these two groups. Result Compared to those with normal baseline volumes, C9orf72 expansion carriers at CDR-GS=0.5 showed significantly higher CDR-SOB scores at follow-up if they had abnormal volumes in the total frontal (+5 points), orbitofrontal (+3), dorsolateral prefrontal (+6), or anterior cingulate (+4) cortices, the basal-paralaminar amygdala region (+2), CA1 region of the hippocampus (+4), total hippocampus (+6), cerebellar lobule VIIIb (+4), or lateral ventricles (+8). GRN mutation carriers showed significantly higher CDR-SOB scores if their volumes were abnormal in the frontal (+10), parietal (+14), insula (+8), orbitofrontal (+12), or medial parietal (+7) cortices, or the total hippocampus (+10). MAPT mu

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Entrainment of overground human walking to mechanical perturbations at the ankle joint

Entrainment of overground human walking to mechanical pertur...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Julieth Ochoa Dagmar Sternad Neville Hogan Department of Mechanical Engineering Massachusetts Institute of Technology (MIT) Cambridge MA USA Departments of Biology Electrical and Computer Engineering and Physics Northeastern University (NEU) Boston MA USA Departments of Mechanical Engineering and Brain and Cognitive Science Massachusetts Institute of Technology (MIT) Cambridge MA USA

ISBN: (纸本)9781509032884

Unlike upper-extremity robotic therapy, robotic therapy of lower extremities has not matched the effectiveness of human-administered approaches. We hypothesize that this may stem from inadvertent interference with natural movement control and investigated the oscillatory dynamics of human locomotion. Specifically, we assessed gait entrainment to periodic mechanical perturbations. Because the treadmills used in most studies necessarily interact with the dynamics of natural walking, we compared our experimental intervention during gait entrainment in treadmill and overground walking. Fourteen healthy subjects walked overground and on a treadmill while wearing an exoskeletal ankle robot which exerted short plantarflexion torque pulses at periods 50 ms shorter or longer than the subjects' preferred stride period. Entrainment to the periodic perturbation occurred in all conditions, however more readily in overground walking. In all cases, the stride period phase-locked with the torque pulse at `push-off' such that it assisted propulsion. This entrainment of the stride period and its sensitivity to context indicate the subtlety and adaptability of human walking. Our observations suggest new avenues for gait rehabilitation and implications for exoskeleton design and legged locomotion research.

关键词： Legged locomotion Torque Knee Medical treatment Oscillators Convergence

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A deep neural network for time-domain signal reconstruction 40

A deep neural network for time-domain signal reconstruction

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Wang, Yuxuan Wang, Deliang Department of Computer Science and Engineering Ohio State University United States Center for Cognitive and Brain Sciences Ohio State University United States

ISBN: (纸本)9781467369978

Supervised speech separation has achieved considerable success recently. Typically, a deep neural network (DNN) is used to estimate an ideal time-frequency mask, and clean speech is produced by feeding the mask-weighted output to a resynthesizer in a subsequent step. So far, the success of DNN-based separation lies mainly in improving human speech intelligibility. In this work, we propose a new deep network that directly reconstructs the time-domain clean signal through an inverse fast Fourier transform layer. The joint training of speech resynthesis and mask estimation yields improved objective quality while maintaining the objective intelligibility performance. The proposed system significantly outperforms a recent non-negative matrix factorization based separation system in both objective speech intelligibility and quality. © 2015 IEEE.

关键词： Deep neural network speech separation time-domain signal time-frequency masking

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Deep neural networks for estimating speech model activations 40

Deep neural networks for estimating speech model activations

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Williamson, Donald S. Wang, Yuxuan Wang, Deliang Department of Computer Science and Engineering Ohio State University United States Center for Cognitive and Brain Sciences Ohio State University United States

ISBN: (纸本)9781467369978

This paper presents an approach for improving the perceptual quality of speech separated from background noise at low signal-to-noise ratios. Our approach uses two stages of deep neural networks, where the first stage estimates the ideal ratio mask that separates speech from noise, and the second stage maps the ratio-masked speech to the clean speech activation matrices that are used for nonnegative matrix factorization (NMF). Supervised NMF systems make assumptions about the relationship between the activation and basic matrices that do not always hold. Other two-stage approaches combining masking with NMF reconstruction do not account for mask estimation errors. We show that the proposed algorithm achieves higher objective speech quality and intelligibility compared to these related methods. © 2015 IEEE.

关键词： deep neural network nonnegative matrix factorization speech quality speech separation

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROscience 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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Publisher Correction: A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters

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Nature chemical biology 2018年第9期14卷 901-901页

作者： Kiryl D Piatkevich Erica E Jung Christoph Straub Changyang Linghu Demian Park HoJun Suk Daniel R Hochbaum Daniel Goodwin Eftychios Pnevmatikakis Nikita Pak Takashi Kawashima ChaoTsung Yang Jeffrey L Rhoades Or Shemesh Shoh Asano YoungGyu Yoon Limor Freifeld Jessica L Saulnier Clemens Riegler Florian Engert Thom Hughes Mikhail Drobizhev Balint Szabo Misha B Ahrens Steven W Flavell Bernardo L Sabatini Edward S Boyden Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA. Howard Hughes Medical Institute Department of Neurobiology Harvard Medical School Boston MA USA. Department of Electrical Engineering and Computer Science MIT Cambridge MA USA. Harvard-MIT Division of Health Sciences and Technology MIT Cambridge MA USA. Simons Center Data Analysis Simons Foundation New York NY USA. Department of Mechanical Engineering MIT Cambridge MA USA. Janelia Research Campus Howard Hughes Medical Institute Ashburn Virginia USA. Picower Institute for Learning & Memory and Department of Brain & Cognitive Sciences MIT Cambridge MA USA. Department of Molecular and Cellular Biology and Center for Brain Science Harvard University Cambridge MA USA. Department of Neurobiology Faculty of Life Sciences University of Vienna Wien Austria. Department of Cell Biology and Neuroscience Montana State University Bozeman Montana USA. Department of Biological Physics Eotvos University Budapest Hungary. Media Lab Massachusetts Institute of Technology (MIT) Cambridge MA USA. esb@media.mit.edu. Department of Biological Engineering MIT Cambridge MA USA. esb@media.mit.edu. MIT Center for Neurobiological Engineering MIT Cambridge MA USA. esb@media.mit.edu. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. esb@media.mit.edu. MIT McGovern Institute for Brain Research MIT Cambridge MA USA. esb@media.mit.edu.

In the version of this article originally published, the bottom of Figure 4f,g was partially truncated in the PDF. The error has been corrected in the PDF version of this article.

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Deep neural networks for cochannel speaker identification 40

Deep neural networks for cochannel speaker identification

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Zhao, Xiaojia Wang, Yuxuan Wang, Deliang Department of Computer Science and Engineering Ohio State University ColumbusOH United States Center for Cognitive and Brain Sciences Ohio State University ColumbusOH United States

ISBN: (纸本)9781467369978

Speaker identification (SID) in cochannel speech, where two speakers are talking simultaneously over a single recording channel, is a challenging problem. Previous studies address this problem in the anechoic environment under the Gaussian mixture model (GMM) framework. On the other hand, cochannel SID in reverberant conditions has not been addressed. This paper studies cochannel SID in both anechoic and reverberant conditions. We explore deep neural networks (DNNs) for cochannel SID and propose a DNN-based recognition system. Evaluation results demonstrate the proposed DNN-based system outperforms the two state-of-the-art cochannel SID systems in both anechoic and reverberant conditions and various target-to-interferer ratios. © 2015 IEEE.

关键词： Cochannel speaker identification deep neural network Gaussian mixture model reverberation target-to-interferer ratio

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