Sparsification for Monaural Source Separation

[1]  Anat Levin,et al.  User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[3]  Paris Smaragdis,et al.  Convolutive Speech Bases and Their Application to Supervised Speech Separation , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[4]  Jon Barker,et al.  An audio-visual corpus for speech perception and automatic speech recognition. , 2006, The Journal of the Acoustical Society of America.

[5]  B.A. Pearlmutter,et al.  Linear Program Differentiation for Single-Channel Speech Separation , 2006, 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing.

[6]  Barak A. Pearlmutter,et al.  Sparse Representations for the Cocktail Party Problem , 2006, The Journal of Neuroscience.

[7]  Daniel P. W. Ellis,et al.  Model-Based Monaural Source Separation Using a Vector-Quantized Phase-Vocoder Representation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[8]  Michael S. Lewicki,et al.  Efficient auditory coding , 2006, Nature.

[9]  John R. Hershey,et al.  Super-human multi-talker speech recognition: the IBM 2006 speech separation challenge system , 2006, INTERSPEECH.

[10]  Michael I. Jordan,et al.  Blind One-microphone Speech Separation: A Spectral Learning Approach , 2004, NIPS.

[11]  Barak A. Pearlmutter,et al.  Monaural Source Separation Using Spectral Cues , 2004, ICA.

[12]  Paris Smaragdis,et al.  Non-negative Matrix Factor Deconvolution; Extraction of Multiple Sound Sources from Monophonic Inputs , 2004, ICA.

[13]  Yuanqing Li,et al.  Analysis of Sparse Representation and Blind Source Separation , 2004, Neural Computation.

[14]  Masakazu Konishi,et al.  Mechanisms of sound localization in the barn owl (Tyto alba) , 1979, Journal of comparative physiology.

[15]  Te-Won Lee,et al.  A Maximum Likelihood Approach to Single-channel Source Separation , 2003, J. Mach. Learn. Res..

[16]  Terrence J Sejnowski,et al.  Communication in Neuronal Networks , 2003, Science.

[17]  Rémi Gribonval,et al.  Non negative sparse representation for Wiener based source separation with a single sensor , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[18]  Michael Elad,et al.  Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Joseph F. Murray,et al.  Dictionary Learning Algorithms for Sparse Representation , 2003, Neural Computation.

[20]  Tuomas Virtanen,et al.  Sound Source Separation Using Sparse Coding with Temporal Continuity Objective , 2003, ICMC.

[21]  Konrad P. Körding,et al.  Sparse Spectrotemporal Coding of Sounds , 2003, EURASIP J. Adv. Signal Process..

[22]  Mark D. Plumbley,et al.  Automatic Music Transcription and Audio Source Separation , 2002, Cybern. Syst..

[23]  Paul M. Hofman,et al.  Bayesian reconstruction of sound localization cues from responses to random spectra , 2002, Biological Cybernetics.

[24]  Michael S. Lewicki,et al.  Efficient coding of natural sounds , 2002, Nature Neuroscience.

[25]  Eero P. Simoncelli,et al.  Natural signal statistics and sensory gain control , 2001, Nature Neuroscience.

[26]  Barak A. Pearlmutter,et al.  Blind Source Separation by Sparse Decomposition in a Signal Dictionary , 2001, Neural Computation.

[27]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[28]  S. Rickard,et al.  DOA estimation of many W-disjoint orthogonal sources from two mixtures using DUET , 2000, Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No.00TH8496).

[29]  Terrence J. Sejnowski,et al.  Learning Overcomplete Representations , 2000, Neural Computation.

[30]  Kazuya Takeda,et al.  Estimating Head Related Transfer Function Using Multiple Regression Analysis , 2000 .

[31]  Andreas Griewank,et al.  Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition , 2000, Frontiers in applied mathematics.

[32]  Sam T. Roweis,et al.  One Microphone Source Separation , 2000, NIPS.

[33]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[34]  H Farid,et al.  Separating reflections from images by use of independent component analysis. , 1999, Journal of the Optical Society of America. A, Optics, image science, and vision.

[35]  Terrence J. Sejnowski,et al.  Blind source separation of more sources than mixtures using overcomplete representations , 1999, IEEE Signal Processing Letters.

[36]  H. Steven Colburn,et al.  Role of spectral detail in sound-source localization , 1998, Nature.

[37]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[38]  Bruno A. Olshausen,et al.  Inferring Sparse, Overcomplete Image Codes Using an Efficient Coding Framework , 1998, NIPS.

[39]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[40]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[41]  Bhaskar D. Rao,et al.  Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..

[42]  Hagai Attias,et al.  Temporal Low-Order Statistics of Natural Sounds , 1996, NIPS.

[43]  Paul S. Bradley,et al.  Clustering via Concave Minimization , 1996, NIPS.

[44]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[45]  William B. Levy,et al.  Energy Efficient Neural Codes , 1996, Neural Computation.

[46]  S. Sheft,et al.  A simulated “cocktail party” with up to three sound sources , 1996, Perception & psychophysics.

[47]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[48]  William Nick Street,et al.  Breast Cancer Diagnosis and Prognosis Via Linear Programming , 1995, Oper. Res..

[49]  F L Wightman,et al.  Localization using nonindividualized head-related transfer functions. , 1993, The Journal of the Acoustical Society of America.

[50]  Albert S. Bregman,et al.  The Auditory Scene. (Book Reviews: Auditory Scene Analysis. The Perceptual Organization of Sound.) , 1990 .

[51]  F L Wightman,et al.  Headphone simulation of free-field listening. II: Psychophysical validation. , 1989, The Journal of the Acoustical Society of America.

[52]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[53]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[54]  R. Fletcher.  Semi-Definite Matrix Constraints in Optimization , 1985 .

[55]  Robert Dorfman,et al.  The Discovery of Linear Programming , 1984, Annals of the History of Computing.

[56]  B. Speelpenning.  Compiling Fast Partial Derivatives of Functions Given by Algorithms , 1980 .

[57]  R. E. Wengert,et al.  A simple automatic derivative evaluation program , 1964, Commun. ACM.

[58]  S. S. Stevens.  On the psychophysical law. , 1957, Psychological review.