monotoSTEREO.info | RESEARCH (I

RESEARCH [I - L]

Scroll down to find direct links to publicly posted research papers, presentations, etc. in the field of sound source separation and related topics. I have listed them in alphabetical order by title for ease of browsing and have provided these links, which are available on the web, for your convenience. I have provided permalinks for those papers available for a fee. Titles without a link can be found through a web search. Please use the CONTACT page to notify me of any corrections, to supply suggestions for adding any additional pertinent links, or to notify me if you encounter any dead links in this list. Thanks!

ICA-BASED SINGLE CHANNEL AUDIO SEPARATION: NEW BASES AND MEASURES OF DISTANCE [PDF]

Dariusz Mika(1), Piotr Kleczkowski(2), (1)Studio sQuat Professional Sound Studio Recording Pl. Tysiąclecia PP 1, Chełm, Poland, (2)AGH University of Science and Technology, Department of Mechanics and Vibroacoustics, Kraków, Poland (2011)

IMPACT OF LOW-PRECISION DEEP REGRESSION NETWORKS ON SINGLE-CHANNEL SOURCE SEPARATION [permalink]

Enea Ceolini and Shih-Chii Liu, Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland (2017)

IMPACT OF PHASE ESTIMATION ON SINGLE-CHANNEL SPEECH SEPARATION BASED ON TIME-FREQUENCY MASKING [PDF]

Florian Mayer,1,a) Donald S. Williamson,2 Pejman Mowlaee,3 and DeLiang Wang4, 1FH Joanneum – University of Applied Sciences, Graz, Austria,

2Department of Computer Science, Indiana University, Bloomington, Indiana, USA, 3Signal Processing and Speech Communication Lab, Graz University of Technology, Graz, Austria, 4Department of Computer Science and Engineering, Center for Cognitive and Brain Sciences, Ohio State University, Columbus, Ohio, USA (2017)

IMPLEMENTATION AND ASSESSMENT OF JOINT SOURCE SEPARATION AND DEREVERBERATION [PDF]

David Moffat, and Joshua D. Reiss, Center for Digital Music, Queen Mary University of London (2016)

IMPLEMENTATION OF HARMONIC-PERCUSSIVE SOUND SEPARATION FOR AUDACITY [PDF]

Viktor Tamás Erdélyi, National Institute of Informatics, Saarland University, Tokyo, Japan and Saarbrücken, Germany, Nobutaka Ono, National Institute of Informatics, Tokyo, Japan, Shigeki Sagayama, Meiji University, Tokyo, Japan (2015)

IMPLEMENTATION OF HARMONIC-PERCUSSIVE SOUND SEPARATION FOR AUDACITY (poster) [PDF]

IMPLICIT AND EXPLICIT PHASE MODELING IN DEEP LEARNING-BASED SOURCE SEPARATION [PDF]

Manuel Pariente, Université de Lorraine, Nancy, France (2021)

IMPROVED MODELLING OF ATTACK TRANSIENTS IN MUSIC ANALYSIS-RESYNTHESIS [PDF]

Paul Masri, Andrew Bateman, Digital Music Research Group, University of Bristol, Bristol, U.K. (1996)

IMPROVED SINGING VOICE SEPARATION WITH CHROMAGRAM-BASED PITCH-AWARE REMIXING [PDF]

Siyuan Yuan♯∗, Zhepei Wang♭∗, Umut Isik†, Ritwik Giri† Jean-Marc Valin†, Michael M. Goodwin†, Arvindh Krishnaswamy†, † Amazon Web Services, ♯ Stanford University, ♭ University of Illinois at Urbana-Champaign (2022)

IMPROVED VOCAL ISOLATION FROM VARYING REPEATING STRUCTURES FOR MUSICAL SIGNALS

Asutosh Kar, Department of Electronics and Communication Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar, Punjab, India (2023)

IMPROVEMENTS TO PERCUSSIVE COMPONENT EXTRACTION USING NON-NEGATIVE MATRIX FACTORIZATION AND SUPPORT VECTOR MACHINES [PDF]

Eric Battenberg (2009)

IMPROVING CHORAL MUSIC SEPARATION THROUGH EXPRESSIVE SYNTHESIZED DATA FROM SAMPLED INSTRUMENTS [PDF]

Ke Chen1, Hao-Wen Dong1, Yi Luo2, Julian McAuley1, Taylor Berg-Kirkpatrick1, Miller Puckette1, Shlomo Dubnov1, 1UC San Diego, USA, 2Tencent AI Lab, China (2022)

IMPROVING DNN-BASED MUSIC SOURCE SEPARATION USING PHASE FEATURES [PDF]

Joachim Muth 1, Stefan Uhlich 2, Nathanaël Perraudin 3, Thomas Kemp 2, Fabien Cardinaux 2, Yuki Mitsufuji 4, 1École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 2Sony European Technology Center (EuTEC), Stuttgart, Germany, 3Swiss Data Science Center, EPFL and ETH Zürich, Switzerland, 4Sony Corporation, Audio Technology Development Department, Tokyo, Japan (2018)

IMPROVING MELODY EXTRACTION USING PROBABILISTIC LATENT COMPONENT ANALYSIS [PDF]

Jinyu Han⋆ ∗, Ching-Wei Chen†, † Gracenote, Inc., Emeryville, California, ⋆Northwestern University, Evanston, Illinois (2011)

IMPROVING MELODY EXTRACTION USING PROBABILISTIC LATENT COMPONENT ANALYSIS (presentation) [PDF]

Jinyu. Han1 Ching-Wei. Chen2, 1Interactive Audio Lab, Northwestern Univsersity, USA, 2Media Technology Lab Gracenote, Inc (2011)

IMPROVING MUSIC SOURCE SEPARATION BASED ON DNNS THROUGH DATA AUGMENTATION AND NETWORK BLENDING [permalink]

Stefan Uhlich1, Marcello Porcu1, Franck Giron1, Michael Enenkl1, Thomas Kemp1, Naoya Takahashi2 and Yuki Mitsufuji2, 1 Sony European Technology Center (EuTEC), Stuttgart, Germany, 2 Sony Corporation, Audio Technology Development Department, Tokyo, Japan (2017)

IMPROVING MUSIC SOURCE SEPARATION BASED ON DNNS THROUGH DATA AUGMENTATION AND NETWORK BLENDING (poster) [PDF]

IMPROVING MUSIC SOURCE SEPARATION THROUGH FEATURE-INFORMED LATENT SPACE REGULARIZATION

Yun-Ning Hung and Alexander Lerch, Georgia Institute of Technology (2022)

IMPROVING POLYPHONIC MELODY EXTRACTION BY DYNAMIC PROGRAMMING BASED DUAL F0 TRACKING [PDF]

Vishweshwara Rao and Preeti Rao, Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India (2009)

IMPROVING SCORE-INFORMED SOURCE SEPARATION FOR CLASSICAL MUSIC THROUGH NOTE REFINEMENT [PDF]

Marius Miron, Julio José Carabias-Orti, Jordi Janer, Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain (2015)

IMPROVING SINGING VOICE SEPARATION USING ATTRIBUTE-AWARE DEEP NETWORK [PDF]

Rupak Vignesh Swaminathan, Alexa Speech, Amazon.com, Inc., United States, Alexander Lerch, Center for Music Technology, Georgia Institute of Technology, United States (2019)

IMPROVING SINGING VOICE SEPARATION USING CURRICULUM LEARNING ON RECURRENT NEURAL NETWORKS [PDF direct download]

Seungtae Kang 1, Jeong-Sik Park 2, Gil-Jin Jang 1, 1 School of Electronics Engineering, Kyungpook National University, Daegu, Korea, 2 Department of English Linguistics and Language Technology, Hankuk University of Foreign Studies, Seoul, Korea (2020)

IMPROVING SINGING VOICE SEPARATION USING DEEP U-NET AND WAVE-U-NET WITH DATA AUGMENTATION [PDF]

Alice Cohen-Hadria & Axel Roebel, UMR STMS 9912 Sorbonne Université, IRCAM, CNRS, Paris, France, Geoffroy Peeters, LTCI Télécom ParisTech, Université Paris-Saclay, Paris, France (2019)

IMPROVING SINGING VOICE SEPARATION WITH THE WAVE-U-NET USING MINIMUM HYPERSPHERICAL ENERGY [PDF]

Joaquin Perez-Lapillo, Oleksandr Galkin, Tillman Weyde, Department of Computer Science, City, University of London (2019)

IMPROVING SINGLE-NETWORK SINGLE-CHANNEL SEPARATION OF MUSICAL AUDIO WITH CONVOLUTIONAL LAYERS [PDF]

Gerard Roma, Owen Green, and Pierre Alexandre Tremblay, Centre for Research in New Music, University of Huddersfield, Huddersfield, UK (2018)

IMPROVING SINGLE-NETWORK SINGLE-CHANNEL SEPARATION OF MUSICAL AUDIO WITH CONVOLUTIONAL LAYERS (examples)

Gerard Roma, Owen Green, and Pierre Alexandre Tremblay, Centre for Research in New Music, University of Huddersfield, Huddersfield, UK (2018)

IMPROVING SOURCE SEPARATION BY EXPLICITLY MODELING DEPENDENCIES BETWEEN SOURCES [PDF]

Ethan Manilow1,2,∗, Curtis Hawthorne1, Cheng-Zhi Anna Huang1, Bryan Pardo2, Jesse Engel1, 1Google Research, Brain Team, 2Northwestern University (2022)

IMPROVING TIME–FREQUENCY SPARSITY FOR ENHANCED AUDIO SOURCE SEPARATION IN DEGENERATE UNMIXING ESTIMATION TECHNIQUE ALGORITHM [permalink]

Shahin M. Abdulla, Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Nagercoil, India, J. Jayakumari, Department of Electronics and Communication Engineering, Mar Baselios College of Engineering and Technology, Thiruvananthapuram, India (2022)

IMPROVING THE PERCEPTUAL QUALITY OF SINGLE-CHANNEL BLIND AUDIO SOURCE SEPARATION [PDF]

Tobias Stokes, Institute of Sound Recording, Faculty of Arts and Human Sciences, University of Surrey, Guildford, UK (2015)

IMPROVING UNIVERSAL SOUND SEPARATION USING SOUND CLASSIFICATION PRESENTATION [permalink]

Efthymios Tzinis1,2, Scott Wisdom1, John R. Hershey1, Aren Jansen1 and Daniel P. W. Ellis1, 1Google Research, 2University of Illinois at Urbana-Champaign (2020)

IMPROVING UNIVERSAL SOUND SEPARATION USING SOUND CLASSIFICATION PRESENTATION (slides) [PDF]

Efthymios Tzinis1,2, Scott Wisdom1, John R. Hershey1, Aren Jansen1 and Daniel P. W. Ellis1, 1Google Research, 2University of Illinois at Urbana-Champaign (2020)

IMPROVING UNIVERSAL SOUND SEPARATION USING SOUND CLASSIFICATION PRESENTATION (video presentation)

Efthymios Tzinis1,2, Scott Wisdom1, John R. Hershey1, Aren Jansen1 and Daniel P. W. Ellis1, 1Google Research, 2University of Illinois at Urbana-Champaign (2020)

IMPROVING VOICE SEPARATION BY BETTER CONNECTING CONTIGS [PDF]

Nicolas Guiomard-Kagan1, Mathieu Giraud2, Richard Groult1, Florence Leve ́1,2, 1 MIS, Univ. Picardie Jules Verne, Amiens, France, 2 CRIStAL, UMR CNRS 9189, Univ. Lille, Lille, France (2016)

INCORPORATING PHASE INFORMATION FOR SOURCE SEPARATION VIA SPECTROGRAM FACTORIZATION [PDF]

R. Mitchell Parry and Irfan Essa, Georgia Institute of Technology, College of Computing / GVU Center, Atlanta, Georgia (2007)

INCORPORATING PRIOR INFORMATION IN NONNEGATIVE MATRIX FACTORIZATION FOR AUDIO SOURCE SEPARATION [PDF]

Emad Mounir Grais Girgis, Sabanci University (2013)

INCREMENTAL APPROACH TO NMF BASIS ESTIMATION FOR AUDIO SOURCE SEPARATION [permalink]

Kisoo Kwon and Inkyu Choi, Dept. of Electrical and Computer Engineering and the INMC, Seoul National University, Seoul, Korea,

Jong Won Shin, School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Korea (2017)

INDEPENDENT DEEPLY LEARNED MATRIX ANALYSIS FOR DETERMINED AUDIO SOURCE SEPARATION [permalink]

Naoki Makishima, Shinichi Mogami, Norihiro Takamune, Hayato Sumino, Shinnosuke Takamichi, Hiroshi Saruwatari, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan, Daichi Kitamura, National Institute of Technology, Kagawa College, Kagawa, Japan, Nobutaka Ono, Graduate School of System Design, Tokyo Metropolitan University, Tokyo, Japan (2019)

INDEPENDENT DEEPLY LEARNED TENSOR ANALYSIS FOR DETERMINED AUDIO SOURCE SEPARATION [permalink]

Naoki Narisawa1, Rintaro Ikeshita2, Norihiro Takamune1, Daichi Kitamura3, Tomohiko Nakamura1, Hiroshi Saruwatari1, Tomohiro Nakatani2, 1 Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan, 2 NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan, 3 National Institute of Technology, Kagawa College, Kagawa, Japan (2021)

INFINITE POSITIVE SEMIDEFINITE TENSOR FACTORIZATION FOR SOURCE SEPARATION OF MIXTURE SIGNALS [PDF]

Kazuyoshi Yoshii, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan, Ryota Tomioka, The University of Tokyo, Tokyo, Japan, Daichi Mochihashi, The Institute of Statistical Mathematics (ISM), Tokyo, Japan, Masataka Goto, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan (2013)

INFINITE POSITIVE SEMIDEFINITE TENSOR FACTORIZATION FOR SOURCE SEPARATION OF MIXTURE SIGNALS (poster) [PDF]
Kazuyoshi Yoshii, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan, Ryota Tomioka, The University of Tokyo, Tokyo, Japan, Daichi Mochihashi, The Institute of Statistical Mathematics (ISM), Tokyo, Japan, Masataka Goto, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan (2013)

INFINITE POSITIVE SEMIDEFINITE TENSOR FACTORIZATION FOR SOURCE SEPARATION OF MIXTURE SIGNALS (video w/slides)
Kazuyoshi Yoshii, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan, Ryota Tomioka, The University of Tokyo, Tokyo, Japan, Daichi Mochihashi, The Institute of Statistical Mathematics (ISM), Tokyo, Japan, Masataka Goto, National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan (2013)

INFINITE PROBABILISTIC LATENT COMPONENT ANALYSIS FOR AUDIO SOURCE SEPARATION [permalink]

Kazuyoshi Yoshii1,2, Eita Nakamura1, Katsutoshi Itoyama1, Masataka Goto3, 1Kyoto University, 2RIKEN, 3National Institute of Advanced Industrial Science and Technology (AIST) (2017)

INFINITE-STATE SPECTRUM MODEL FOR MUSIC SIGNAL ANALYSIS [permalink]

Masahiro Nakano†, Jonathan Le Roux‡, Hirokazu Kameoka‡, Nobutaka Ono†, Shigeki Sagayama†

†Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan, ‡NTT Communication Science Laboratories, NTT Kanagawa, Japan (2011)

INFLUENCE OF DIFFERENT INPUT FEATURES ON MUSICAL SOURCE SEPARATION PERFORMANCE [PDF]

Paul A. Bereuter1, Alois Sontacchi1, 1 Institute for Electronic Music and Acoustics, Graz, Osterreich (2023)

INFORMATION THEORETIC APPROACHES TO SOURCE SEPARATION [PDF]

Paris J. Smaragdis, Berklee College of Music, Boston (1997)

INFORMED AUDIO SOURCE SEPARATION [permalink]

Gaël Richard, Institut Mines-Télécom, Télécom ParisTech, CNRS/LTCI, Paris, France (2014)

INFORMED AUDIO SOURCE SEPARATION: A COMPARITIVE STUDY [PDF]

Antoine Liutkus, Stanislaw Gorlow, Nicolas Sturmel, Shuhua Zhang, Laurent Girin, Roland Badeau, Laurent Daudet, Sylvain Marchand, Gaël Richard (2012)

INFORMED AUDIO SOURCE SEPARATION WITH DEEP LEARNING IN LIMITED DATA SETTINGS (thesis) [PDF direct download]

Kilian Schulze-Forster, Télécom Paris (2021)

INFORMED AUDIO SOURCE SEPARATION WITH DEEP LEARNING IN LIMITED DATA SETTINGS (presentation)

Kilian Schulze-Forster, Télécom Paris (2021)

INFORMED GROUP-SPARSE REPRESENTATION FOR SINGING VOICE SEPARATION [permalink]

Tak-Shing T. Chan and Yi-Hsuan Yang, Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan (2017)

INFORMED MONAURAL SOURCE SEPARATION OF MUSIC BASED ON CONVOLUTIONAL SPARSE CODING [PDF]

Ping-Keng Jao†, Yi-Hsuan Yang†, Brendt Wohlberg*, Research Center for Information Technology Innovation, Academia Sinica, Taiwan, *Theoretical Division, Los Alamos National Laboratory, USA (2015)

INFORMED MONAURAL SOURCE SEPARATION OF MUSIC BASED ON CONVOLUTIONAL SPARSE CODING (poster) [PDF]
Ping-Keng Jao†, Yi-Hsuan Yang†, Brendt Wohlberg*, Research Center for Information Technology Innovation, Academia Sinica, Taiwan, *Theoretical Division, Los Alamos National Laboratory, USA (2015)

INFORMED MONAURAL SOURCE SEPARATION OF MUSIC BASED ON CONVOLUTIONAL SPARSE CODING (sound examples)
Ping-Keng Jao†, Yi-Hsuan Yang†, Brendt Wohlberg*, Research Center for Information Technology Innovation, Academia Sinica, Taiwan, *Theoretical Division, Los Alamos National Laboratory, USA (2015)

INFORMED MULTIPLE-F0 ESTIMATION APPLIED TO MONAURAL AUDIO SOURCE SEPARATION [PDF]

Dominique Fourer, LaBRI – CNRS University of Bordeaux 1, France, Sylvain Marchand, Lab-STICC – CNRS, University of Brest, France (2012)

INFORMED MULTIPLE-F0 ESTIMATION APPLIED TO MONAURAL AUDIO SOURCE SEPARATION (experimental results)
Dominique Fourer, LaBRI – CNRS University of Bordeaux 1, France, Sylvain Marchand, Lab-STICC – CNRS, University of Brest, France (2012)

INFORMED SOURCE SEPARATION: A BAYESIAN TUTORIAL [PDF]

Kevin H. Knuth, Intelligent Systems Division, NASA Ames Research Center, Moffatt Field, California, USA (2005)

INFORMED SOURCE SEPARATION FOR MULTIPLE INSTRUMENTS OF SIMILAR TIMBRE [PDF direct download]

Jakue López Armendáriz, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona (2013)

INFORMED SOURCE SEPARATION FROM MONAURAL MUSIC WITH LIMITED BINARY TIME-FREQUENCY ANNOTATION [PDF]

Il-Young Jeong1 and Kyogu Lee1, 2, 1Music and Audio Research Group, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea, 2Advanced Institutes of Convergence Technology, Suwon, Korea (2015)

INFORMED SOURCE SEPARATION FROM MONAURAL MUSIC WITH LIMITED BINARY TIME-FREQUENCY ANNOTATION (poster) [PDF]
Il-Young Jeong1 and Kyogu Lee1, 2, 1Music and Audio Research Group, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea, 2Advanced Institutes of Convergence Technology, Suwon, Korea (2015)

INFORMED SOURCE SEPARATION OF ORCHESTRA AND SOLOIST [PDF]

Yushen Han, Christopher Raphael, School of Informatics and Computing, Indiana University Bloomington (2010)

INFORMED SOURCE SEPARATION OF ORCHESTRA AND SOLOIST USING MASKING AND UNMASKING (slides)
Yushen Han, Christopher Raphael, School of Informatics and Computing, Indiana University Bloomington (2010)

INFORMED SOURCE SEPARATION: SOURCE CODING MEETS SOURCE SEPARATION [permalink]

Alexey Ozerov 1, Antoine Liutkus 2, Roland Badeau 2 and Gaël Richard 2, 1 INRIA, Centre de Rennes - Bretagne Atlantique, Campus de Beaulieu, Rennes Cedex, France 2Institut Telecom, Telecom ParisTech, Paris, France (2011)

INFORMED SOURCE SEPARATION USING ITERATIVE RECONSTRUCTION [PDF]

Nicolas Sturmel, Laurent Daudet, submitted to the IEEE transactions on Audio, Speech and Language Processing (2012)

INFORMED SOURCE SEPARATION USING LATENT COMPONENTS [permalink]

Antoine Liutkus, Roland Badeau, Gaël Richard, Institut Telecom, Telecom ParisTech, CNRS LTCI (2010)

INFORMED SPECTRAL ANALYSIS FOR ISOLATED AUDIO SOURCE PARAMETERS ESTIMATION [permalink]

Dominique Fourer and Sylvain Marchand, LaBRI CNRS, University of Bordeaux 1, Talence, France (2011)

INFORMED SPECTRAL ANALYSIS FOR ISOLATED AUDIO SOURCE PARAMETERS ESTIMATION (sound results)

Dominique Fourer and Sylvain Marchand, LaBRI CNRS, University of Bordeaux 1, Talence, France (2011)

INFORMED SPECTRAL ANALYSIS FOR UNDER DETERMINED AUDIO SOURCE SEPARATION (slides) [PDF]

Fourer Dominique, LaBRI - Université Bordeaux I, Talence, France (2011)

INITIALIZATION OF NONNEGATIVE MATRIX FACTORIZATION DICTIONARIES FOR SINGLE CHANNEL SOURCE SEPARATION [permalink]

Emad M. Grais and Hakan Erdogan, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey (2013)

INPAINTING OF LONG AUDIO SEGMENTS WITH SIMILARITY GRAPHS [PDF]

Nathanaël Perraudin∗†, Nicki Holighaus∗‡, Piotr Majdak‡ and Peter Balazs‡, †Swiss Data Science Center, EPFL and ETH Zürich, Switzerland, ‡Acoustics Research Institute, Austrian Academy of Sciences, Vienna, Austria (2018)

INPAINTING OF MISSING AUDIO SIGNAL SAMPLES [PDF]

Václav Mach, Brno University of Technology, Faculty of Electrical Engineering and Communication, Department of Telecommunications, Brno, Czech Republic (2016)

INSIDE THE SPECTROGRAM: CONVOLUTIONAL NEURAL NETWORKS IN AUDIO PROCESSING [permalink]

Monika Dörfler and Roswitha Bammel, NuHAG, Faculty of Mathematics, Vienna, Austria, Thomas Grill, Austrian Research Institute for Artificial Intelligence, Vienna, Austria (2017)

INSTRUMENT MODELS AND ITS APPLICATIONS [PDF]

Pedro Vera-Candeas, Francisco J. Cañadas-Quesada, Pablo Cabañas-Molero, Francisco J. Rodríguez Serrano, Universidad de Jaén, Jaén, España (2013)

INSTRUMENT MODELS FOR SOURCE SEPARATION AND TRANSCRIPTION OF MUSIC RECORDINGS (MODÈLES D’INSTRUMENTS POUR LA SÉPARATION DE SOURCES ET LA TRANSCRIPTION D’ENREGISTREMENTS MISICAUX) (in French) [PDF]

Emmanuel Vincent, Université Paris VI – Pierre et Marie Curie École Doctorale Edite, IRCAM – Centre Pompidou (2004)

INSTRUMENT RECOGNITION AND TRANSCRIPTION IN POLYPHONIC MUSIC: DETECTION OF SAXOPHONE MELODY IN A JAZZ QUARTET RECORDING [PDF]

Alain Brenzikofer, École Polytechnique Fédérale de Lausanne, Signal Processing Institute, Lausanne, Switzerland (2004)

INSTRUMENT RECOGNITION BEYOND SEPARATE NOTES - INDEXING CONTINUOUS RECORDINGS [PDF]

Arie A. Livshin, Xavier Rodet, Ircam Centre Pompidou, Paris, France <hal-01161113> (2004)

INSTRUMENT SOUND SEPARATION IN SONGS [permalink]

Khalid Youssef, Peng-Yung Woo, Northern Illinois University, Department of Electrical Engineering, Dekalb, USA (2008)

INSTRUMENT-SPECIFIC MUSIC SOURCE SEPARATION VIA INTERPRETABLE AND PHYSICS-INSPIRED ARTIFICIAL INTELLIGENCE [PDF]

Manuel Planton, BSc, University of Music and Performing Arts, Graz University of Technology, Graz, Austria (2023)

INTEGRATING DILATED CONVOLUTION INTO DENSELSTM FOR AUDIO SOURCE SEPARATION [PDF direct download]

Woon-Haeng Heo 1, Hyemi Kim 2 and Oh-Wook Kwon 1, 1 School of Electronics Engineering, Chungbuk National University, Cheongju 28644, Korea, 2 Creative Content Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea (2021)

INTEGRATION AND ADAPTATION OF HARMONIC AND INHARMONIC MODELS FOR SEPARATING POLYPHONIC MUSICAL SIGNALS [permalink]

Katsutoshi Itoyama,† Masataka Goto,‡ Kazunori Komatani,† Tetsuya Ogata,† Hiroshi G. Okuno†, †Dept. of Intelligence Science and Technology Graduate School of Informatics, Kyoto University Sakyo-ku, Kyoto, Japan, ‡National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba, Ibaraki, Japan (2007)

INTELLIGENT ANALYSIS OF COMPOSITE ACOUSTIC SIGNALS [PDF]

John Chowning and Bernard Mont-Reynaud, Center for Computer Research in Music and Acoustics, Stanford University, Stanford, California (1986)

INTELLIGENT AUDIO SOURCE SEPARATION USING INDEPENDENT COMPONENT ANALYSIS [permalink]

Nikolaos Mitianoudis, Mike Davies, DSP Lab, Queen Mary College, University of London (2002)

INTELLIGENT SINGLE-CHANNEL METHODS FOR MULTI-SOURCE AUDIO ANALYSIS [PDF]

Felix Johannes Weninger, Technische Universität München, Lehrstuhl für Mensch-Maschine-Kommunikation (2014)

INTERACTIVE DEEP SINGING-VOICE SEPARATION BASED ON HUMAN-IN-THE-LOOP ADAPTATION [PDF]

Tomoyasu Nakano, Yuki Koyama, Masahiro Hamasaki, Masataka Goto, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan (2020)

INTERACTIVE DEEP SINGING-VOICE SEPARATION BASED ON HUMAN-IN-THE-LOOP ADAPTATION (website)

Tomoyasu Nakano, Yuki Koyama, Masahiro Hamasaki, Masataka Goto, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan (2020)

INTERACTIVE REFINEMENT OF SUPERVISED AND SEMI-SUPERVISED SOUND SOURCE SEPARATION ESTIMATES [PDF]

Nicholas J. Bryan - Center for Computer Research in Music and Acoustics, Stanford University, Gautham J. Mysore - Adobe Research (2013)

INTERACTIVE SOUND SOURCE SEPARATION [PDF]

Nicholas J. Bryan, Stanford University (2014)

INTERACTIVE USER-FEEDBACK FOR SOUND SOURCE SEPARATION [PDF]

Nicholas J. Bryan, Center for Computer Research in Music and Acoustics, Stanford University, California, USA, Gautham J. Mysore, Adobe Research, San Francisco, California, USA (2013)

INTERFERENCE REDUCTION IN MUSIC RECORDINGS COMBINING KERNEL ADDITIVE MODELLING AND NON-NEGATIVE MATRIX FACTORIZATION [PDF]

Delia Fano Yela1, Sebastian Ewert1, Derry FitzGerald2, Mark Sandler1, Queen Mary University of London, UK1, Nimbus Centre, Cork Institute of Technology, Ireland2 (2016)

INTERLEAVED MULTITASK LEARNING FOR AUDIO SOURCE SEPARATION WITH INDEPENDENT DATABASES [PDF]

Clement S. J. Doire, Olumide Okubadejo, Audionamix, Paris (2019)

INTRODUCING A SIMPLE FUSION FRAMEWORK FOR AUDIO SOURCE SEPARATION [permalink]

Xabier Jaureguiberry, Gaël Richard, Institut Mines-Télécom, Télécom ParisTech, Paris, France, Pierre Leveau, Romain Hennequin, Audionamix, Paris, France, Emmanuel Vincent, Inria, Villers-lès-Nancy, France (2013)

INTRODUCING SOURCE-CONTRASTIVE ESTIMATION: LEARNING DEEP EMBEDDINGS FOR AUDIO SOURCE SEPARATION

Patrick C., Lab41

INVESTIGATING DEEP NEURAL TRANSFORMATIONS FOR SPECTROGRAM-BASED MUSICAL SOURCE SEPARATION [PDF]

Woosung Choi 1, Minseok Kim 2, Jaehwa Chung 3, Daewon Lee 4, Soonyoung Jung 5, 1 Dept. of Computer Science, Korea University, Republic of Korea, 2 Dept. of Computer Science, Korea University, Republic of Korea, 3 Dept. of Computer Science, Korea National Open University, Republic of Korea, 4 Dept. of Computer Engineering, Seokyeong University, Republic of Korea, 5 Dept. of Computer Science, Korea University, Republic of Korea (2019)

INVESTIGATING KERNEL SHAPES AND SKIP CONNECTIONS FOR DEEP LEARNING-BASED HARMONIC-PERCUSSIVE SEPARATION [PDF]

Carlos Lordelo1,2, Emmanouil Benetos1, Simon Dixon1, Sven Ahlbäck2, 1 Centre for Digital Music, Queen Mary University of London, London, UK, 2 Doremir Music Research AB, Stockholm, Sweden (2019)

INVESTIGATING SINGLE-CHANNEL AUDIO SOURCE SEPARATION METHODS BASED ON NON-NEGATIVE MATRIX FACTORIZATION [PDF]

Beiming Wang, Mark D. Plumbley, Centre for Digital Music, Department of Electronic Engineering, Queen Mary, University of London (2006)

INVESTIGATING THE POTENTIAL OF PSEUDO QUADRATURE MIRROR FILTER-BANKS IN MUSIC SOURCE SEPARATION TASKS [PDF]

Stylianos Ioannis Mimilakis, Fraunhofer-IDMT, Ilmenau, Germany, Gerald Schuller, Technical University of Ilmenau, Ilmenau, Germany (2017)

INVESTIGATING U-NETS WITH VARIOUS INTERMEDIATE BLOCKS FOR SPECTROGRAM-BASED SINGING VOICE SEPARATION [PDF]

Woosung Choi1, Minseok Kim1, Jaehwa Chung2, Daewon Lee3, Soonyoung Jung1, 1 Department of Computer Science and Engineering, Korea University, Republic of Korea ,2 Department of Computer Science, Korea National Open University, Republic of Korea, 3 Department of Computer Engineering, Seokyeong University, Republic of Korea (2020)

ISMIR 2019 TUTORIAL: WAVEFORM-BASED MUSIC PROCESSING WITH DEEP LEARNING [PDF]

Sander Dieleman, Jordi Pons, Jongpil Lee (2019)

ISOLATING THE SINGING VOICE FROM MUSIC TRACKS: A DEEP NEURAL NETWORKS APPROACH TO KARAOKE [PDF]

Jonathan Deboosere, Ghent University, Ghent, Belgium (2018)

ISSE: AN INTERACTIVE SOURCE SEPARATION EDITOR [PDF]

Nicholas J. Bryan1, Gautham J. Mysore2, Ge Wang1, 1CCRMA, Stanford University, Stanford, California, USA, 2Adobe Research San Francisco, California, USA (2014)

ISSE – AN INTERACTIVE SOURCE SEPARATION EDITOR, PART I (slides) [PDF]

Nicholas J. Bryan, Stanford University (2014)

ISSE – AN INTERACTIVE SOURCE SEPARATION EDITOR, PART II (slides) [PDF]

Nicholas J. Bryan, Stanford University (2014)

ITAKURA-SAITO NONNEGATIVE MATRIX FACTORIZATION AND FRIENDS FOR MUSIC SIGNAL DECOMPOSITION (slides) [PDF]

Cédric Févotte, CNRS LTCI; Télécom ParisTech (2012)

ITAKURA-SAITO NONNEGATIVE MATRIX FACTORIZATION WITH GROUP SPARSITY [PDF]

Augustin Lefèvre⋆†, Francis Bach⋆, Cèdric Fèvotte†, ⋆ INRIA / ENS - Sierra team, † CNRS LTCI / Telecom ParisTech (2011)

ITERATIVE STRUCTURED SHRINKAGE ALGORITHMS FOR STATIONARY/TRANSIENT AUDIO SEPARATION [PDF]

Kai Siedenburg and Simon Doclo, Dept. of Medical Physics and Acoustics, Cluster of Excellence Hearing4All, University of Oldenburg, Germany (2017)

ITERATIVE MONAURAL AUDIO SOURCE SEPARATION FOR SUBSPACE GROUPING [PDF]

Martin Spiertz and Volker Gnann, Institut für Nachrichtentechnik, RWTH Aachen University, Aachen, Germany (2009)

ITERATIVE PHASE ESTIMATION FOR THE SYNTHESIS OF SEPARATED SOURCES FROM SINGLE-CHANNEL MIXTURES [permalink]

David Gunawan & D. Sen, School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, Australia (2010)

ITERATIVE SINUSOIDAL-BASED PARTIAL PHASE RECONSTRUCTION IN SINGLE-CHANNEL SOURCE SEPARATION [PDF]

Mario Kaoru Watanabe and Pejman Mowlaee, Signal Processing and Speech Communication Laboratory, Graz University of Technology, Graz, Austria (2013)

ITERATIVE STRUCTURED SHRINKAGE ALGORITHMS APPLIED TO STATIONARY/TRANSIENT SEPARATION

Kai Siedenburg and Simon Doclo, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany (2017)

J-NET: RANDOMLY WEIGHTED U-NET FOR AUDIO SOURCE SEPARATION [PDF]

Bo-Wen Chen† Yen-Min Hsu* Hung-Yi Lee†*, *Department of Electrical Engineering, National Taiwan University, †Graduate Institute of Communication Engineering, National Taiwan University (2019)

JOINT AMPLITUDE AND PHASE REFINEMENT FOR MONAURAL SOURCE SEPARATION [permalink]

Yoshiki Masuyama, Kohei Yatabe, Kento Nagatomo, Yasuhiro Oikawa, Waseda University, Tokyo, Japan (2020)

JOINT AUDIO INPAINTING AND SOURCE SEPARATION [PDF]

Cagdas Bilen, Alexey Ozerov, Patrick Pérez, Technicolor, Cesson Sévigné, France, The 12th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2015), Aug 2015, Liberec, Czech Republic <hal-01160438> (2015)

JOINT AUDIO SOURCE SEPARATION AND DEREVERBERATION BASED ON MULTICHANNEL FACTORIAL HIDDEN MARKOV MODEL [permalink]

Takuya Higuchi1) and Hirokazu Kameoka1),2), 1)Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan,

2)NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Kanagawa, Japan (2014)

JOINTIST: JOINT LEARNING FOR MULTI-INSTRUMENT TRANSCRIPTION AND ITS APPLICATIONS [PDF]

Kin Wai Cheuk∗1,3, Keunwoo Choi2, Qiuqiang Kong4, Bochen Li4, Minz Won4, Amy Hung4, Ju-Chiang Wang4, Dorien Herremans1, 1 Singapore University of Technology and Design, 2 Gaudio Lab, 3 Agency for Science, Technology and Research, Singapore, 4 ByteDance (2022)

JOINTLY DETECTING AND SEPARATING SINGING VOICE: A MULTI-TASK APPROACH [PDF]

Daniel Stoller1, Sebastian Ewert2, and Simon Dixon1, 1 Queen Mary University of London, London, United Kingdom, 2 Spotify, London, United Kingdom (2018)

JOINTLY RECOGNIZING SPEECH AND SINGING VOICES BASED ON MULTI-TASK AUDIO SOURCE SEPARATION [PDF]

Ye Bai1∗, Chenxing Li1,2∗, Hao Li1, Yuanyuan Zhao1, Xiaorui Wang1, 1Institute of Automation, Chinese Academy of Sciences, Beijing, China, 2Tencent AI Lab, Beijing, China (2024)

JOINT OPTIMIZATION OF MASKS AND DEEP RECURRENT NEURAL NETWORKS FOR MONAURAL SOURCE SEPARATION [PDF]

Po-Sen Huang, Mark Hasegawa-Johnson, Department of Electrical and Computer Engineering, University of Illinois at Urbana- Champaign, Illinois, USA, Minje Kim, Department of Computer Science, University of Illinois at Urbana-Champaign, Illinois, USA, Paris Smaragdis, Department of Computer Science and Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Illinois, USA, and Adobe Research (2015)

JOINT SINGING VOICE SEPARATION AND F0 ESTIMATION WITH DEEP U-NET ARCHITECTURES [permalink]

Andreas Jansson, City, University of London / Spotify Inc, New York, USA, Rachel M. Bittner, Spotify Inc, New York, USA, Sebastian Ewert, Spotify Inc, London, UK, Tillman Weyde, City, University of London, London, UK (2019)

KERNEL ADDITIVE MODELLING FOR SOURCE SEPARATION (results)

A. Liutkus, D. FitzGerald, Z. Rafii, B. Pardo and L. Daudet (2014)

KERNEL ADDITIVE MODELS FOR SOURCE SEPARATION [permalink]

Antoine Liutkus∗1,2,3, Derry FitzGerald4, Zafar Rafii5, Bryan Pardo5, Laurent Daudet6, 1Inria, Villers-lès-Nancy, France, 2Université de Lorraine, LORIA, Villers-lès-Nancy, France, 3CNRS, LORIA, Villers-lès-Nancy, France, 4NIMBUS Centre, Cork Institute of Technology, Ireland, 5Northwestern University, Evanston, IL, USA, 6Institut Langevin, Paris Diderot Univ., France (2014)

KERNEL SPECTROGRAM MODELS FOR SOURCE SEPARATION [PDF]

Antoine Liutkus∗1,2,3, Zafar Rafii4, Bryan Pardo4, Derry FitzGerald5, Laurent Daudet6, 1Inria, Villers-lès-Nancy, France, 2Université de Lorraine, LORIA, Villers-lès-Nancy, France, 3CNRS, LORIA, Villers-lès-Nancy, France, 4Northwestern University, Evanston, IL, USA, 5NIMBUS Centre, Cork Institute of Technology, Ireland 6Institut Langevin, Paris Diderot Univ., France <hal-00959384v3> (2014)

KERNEL SPECTROGRAM MODELS FOR SOURCE SEPARATION (slides) [PDF]

Antoine Liutkus1, Zafar Rafii2, Bryan Pardo2 Derry FitzGerald3, Laurent Daudet4, 1Inria, Universit ́e de Lorraine, LORIA, France 2Northwestern University, Evanston, IL, USA 3NIMBUS Centre, Cork Institute of Technology, Ireland 4Institut Langevin, Paris Diderot Univ., France (2014)

KUIELAB-MDX-NET: A TWO-STREAM NEURAL NETWORK FOR MUSIC DEMIXING [PDF]

Minseok Kim∗1, Woosung Choi†2, Jaehwa Chung3, Daewon Lee4, and Soonyoung Jung‡1, 1 Korea University, 2 Queen Mary University of London, 3 Korea National Open University, 4 Seokyeong University (2021)

KURTOSIS-BASED PROJECTION PURSUIT FOR SIGNAL SEPARATION OF TRADITIONAL MUSICAL INSTRUMENTS [PDF]

1Atik Wintarti, 2Yoyon K. Suprapto, 3Wirawan, 1,2,3Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia, 1Department of Mathematics, Universitas Negeri Surabaya, Indonesia (2016)

L’ANALYSE PROBABILISTE EN COMPOSANTES LATENTES ET SES ADAPTATIONS AUX SIGNAUX MUSICAUX. APPLICATION À LA TRANSCRIPTION AUTOMATIQUE DE MUSIQUE ET À LA SÉPARATION DE SOURCES. (in French) [PDF]

Benoit Fuentes, TELECOM ParisTech, école de l’Institut Mines-Télécom, Paris, France (2013)

LANGUAGE-GUIDED AUDIO-VISUAL SOURCE SEPARATION VIA TRIMODAL CONSISTENCY [PDF]

Reuben Tan1, Arijit Ray1, Andrea Burns1, Bryan A. Plummer1, Justin Salamon2, Oriol Nieto2, Bryan Russell2, Kate Saenko1,3, 1Boston University, 2Adobe Research, 3MIT-IBM Watson AI Lab, IBM Research (2023)

LASAFT: LATENT SOURCE ATTENTIVE FREQUENCY TRANSFORMATION FOR CONDITIONED SOURCE SEPARATION [PDF]

Woosung Choi⋆ , Minseok Kim⋆, Jaehwa Chung†, Soonyoung Jung⋆, ⋆ Department of Computer Science, Korea University, † Department of Computer Science, Korea National Open University (2020)

LASAFT: LATENT SOURCE ATTENTIVE FREQUENCY TRANSFORMATION FOR CONDITIONED SOURCE SEPARATION (demos)

Woosung Choi⋆ , Minseok Kim⋆, Jaehwa Chung†, Soonyoung Jung⋆, ⋆ Department of Computer Science, Korea University, † Department of Computer Science, Korea National Open University (2020)

LATENT AUTOREGRESSIVE SOURCE SEPARATION [PDF]

Emilian Postolache1, Giorgio Mariani1, Michele Mancusi1, Andrea Santilli1, Luca Cosmo2, Emanuele Rodola`1, 1 Sapienza University of Rome, Italy,

2 Ca’ Foscari University of Venice, Italy (2023)

LATENT DIRICHLET DECOMPOSITION FOR SINGLE CHANNEL SPEAKER SEPARATION [PDF]

Bhiksha Raj, Paris Smaragdis, Mitsubishi Electric Research Labs, Cambridge, Massachusetts, Madhusudana V. S. Shashanka, Boston University Hearing Research Center, Boston, Massachusetts (2006)

LATENT SPACE REGULARIZATION FOR MUSIC SOURCE SEPARATION

Yun-Ning (Amy) Hung, Center for Music Technology, Georgia Institute of Technology (2022)

LATENT TIME-FREQUENCY COMPONENT ANALYSIS: A NOVEL PITCH-BASED APPROACH FOR SINGING VOICE SEPARATION [permalink]

Xiu Zhang, Wei Li, Bilei Zhu, School of Computer Science, Fudan University, Shanghai, China (2015)

LATENT VARIABLE FRAMEWORK FOR MODELING AND SEPARATING SINGLE-CHANNEL ACOUSTIC SOURCES [PDF]
Madhusudana Shashanka, Boston University, Graduate School of Arts and Sciences, Boston, Massachusetts (2007)

LATENT VARIABLE MODELS FOR AUDIO SPECTRA (slides) [PDF]

Paris Smaragdis, Adobe Systems Inc. (2008)

LEAD INSTRUMENT EXTRACTION (demo)

Gautham J. Mysore

LEARNED COMPLEX MASKS FOR MULTI-INSTRUMENT SOURCE SEPARATION [PDF]

Andreas Jansson2, Rachel M. Bittner1, Nicola Montecchio1, Tillman Weyde2, 1 Spotify, 2 City University of London (2021)

LEARNING A DISCRIMINATIVE DICTIONARY FOR SINGLE-CHANNEL SPEECH SEPARATION [permalink]

Guangzhao Bao ; Dept. of Electron. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Hefei, China ; Yangfei Xu ; Zhongfu Ye (2014)

LEARNING A JOINT EMBEDDING SPACE OF MONOPHONIC AND MIXED MUSIC SIGNALS FOR SINGING VOICE [PDF]

Kyungyun Lee, Juhan Nam, Graduate School of Culture Technology, KAIST (2019)

LEARNING BLIND ONE-MICROPHONE SPEECH SEPARATION (presentation) [PDF]

Francis Bach, Ecole des Mines de Paris, Michael Jordan, UC Berkeley (2006)

LEARNING LONG-TERM FILTER BANKS FOR AUDIO SOURCE SEPARATION AND AUDIO SCENE CLASSIFICATION [PDF]

Tens Zhang, Ji Wu, Department of Electronic Engineering, Tsinghua University, Beijing, China (2018)

LEARNING MUSICAL INSTRUMENTS FROM MIXTURES OF AUDIO WITH WEAK LABELS [PDF]

David Little and Bryan Pardo, EECS Department Northwestern University, Evanston, Illinois (2008)

LEARNING TECHNIQUES FOR IDENTIFYING VOCAL REGIONS IN MUSIC USING THE WAVELET TRANSFORMATION, VERSION 1.0 [PDF]

Michael J. Henry, B.S., Graduate School of Arts and Sciences, Georgetown University (2011)

LEARNING TO PINPOINT SINGING VOICE FROM WEAKLY LABELED EXAMPLES [PDF]

Jan Schlüter, Austrian Research Institute for Artificial Intelligence, Vienna (2016)

LEARNING TO SEPARATE OBJECT SOUNDS BY WATCHING UNLABELED VIDEO

Ruohan Gao1, Rogerio Feris2, Kristen Grauman1, 1The University of Texas at Austin, 2IBM Research (2018)

LEARNING TO SEPARATE OBJECT SOUNDS BY WATCHING UNLABELED VIDEO [PDF]

Ruohan Gao1, Rogerio Feris2, Kristen Grauman1, 1The University of Texas at Austin, 2IBM Research (2018)

LEARNING TO SEPARATE VOCALS FROM POLYPHONIC MIXTURES VIA ENSEMBLE METHODS AND STRUCTURED OUTPUT PREDICTION [PDF]

M. McVicar, R. Santos-Rodríguez, T. De Bie, Intelligent Systems Laboratory, Department of Engineering Mathematics, University of Bristol (2016)

LEARNING WITH NONNEGATIVE MATRIX FACTORIZATIONS

Nicolas Gillis, Associate Professor, Department of Mathematics and Operational Research, University of Mons, Belgium (2019)

LECTURE 9: SOURCE SEPARATION (slides) [PDF]

Yi-Hsuan Yang Ph.D., Music & Audio Computing Lab, Research Center for IT Innovation, Academia Sinica (2016)

LECTURE 10: HARMONIC/PERCUSSIVE SEPARATION [PDF]

Yi-Hsuan Yang Ph.D., Music & Audio Computing Lab, Research Center for IT Innovation, Academia Sinica (2016)

LECTURE 11: SINGING VOICE SEPARATION (slides) [PDF]

Yi-Hsuan Yang Ph.D., Music & Audio Computing Lab, Research Center for IT Innovation, Academia Sinica (2016)

LEVERAGING CATEGORY INFORMATION FOR SINGLE-FRAME VISUAL SOUND SOURCE SEPARATION [PDF]

Lingyu Zhu, Esa Rahtu, Computer Vision Group, Tampere University, Finland (2021)

LEVERAGING SYNTHETIC DATA FOR IMPROVING CHAMBER ENSEMBLE SEPARATION [PDF]

Saurjya Sarkar, Louise Thorpe, Emmanouil Benetos, Mark Sandler, Centre for Digital Music Queen Mary University of London London, UK (2023)

LÉVY NMF FOR ROBUST NONNEGATIVE SOURCE SEPARATION [PDF]

Paul Magron, Institut Mines-Télécom, Roland Badeau, Télécom ParisTech / CNRS LTCI, Antoine Liutkus, National Institute for Research in Computer Science and Control (2016)

LÉVY NMF : UN MODÈLE ROBUSTE DE SÉPARATION DE SOURCES NON-NÉGATIVES (in French) [PDF]

Paul Magron1,2, Roland Badeau2, Antoine Liutkus3 ∗, 1Signal Processing Laboratory, Tampere University of Technology (TUT), Finland, Télécom ParisTech, Université Paris-Saclay, Paris, France, 3Inria, Nancy Grand-Est, Multispeech team, LORIA UMR 7503, France (2017)

LIGHTSAFT: LIGHTWEIGHT LATENT SOURCE AWARE FREQUENCY TRANSFORM FOR SOURCE SEPARATION [PDF]

Yeong-Seok Jeong∗1, Jinsung Kim†1, Woosung Choi2, Jaehwa Chung3, and Soonyoung Jung‡1, 1 Korea University, 2 Queen Mary University of London, 3 Korea National Open University (2021)

LIGHTWEIGHT END-TO-END DEEP LEARNING MODEL FOR MUSIC SOURCE SEPARATION [PDF]

Yao-Ting Wang1, Yi-Xing Lin1, Kai-Wen Liang1, Tzu-Chiang Tai2, and Jia-Ching Wang1, 1Department of Computer Science and Information Engineering, National Central University, Taiwan, 2Department of Computer Science and Information Engineering, Providence University, Taiwan (2022)

LIVE AND NON-REAL-TIME SOURCE SEPARATION EFFECTS FOR ELECTROACOUSTIC MUSIC

Nick Collins, Durham University, Durham, UK (2014)

LOG-NORMAL MATRIX FACTORIZATION WITH APPLICATION TO SPEECH-MUSIC SEPARATION [PDF]

Takuya Yoshioka, Daichi Sakaue, NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan (2012)

LONG-TERM REVERBERATION MODELING FOR UNDER-DETERMINED AUDIO SOURCE SEPARATION WITH APPLICATION TO VOCAL MELODY EXTRACTION [PDF]

Romain Hennequin, Deezer R&D, Paris, France, François Rigaud, Audionamix R&D, Paris, France (2016)

LOOKING TO LISTEN AT THE COCKTAIL PARTY: A SPEAKER-INDEPENDENT AUDIO-VISUAL MODEL FOR SPEECH SEPARATION [PDF]

Ariel Ephrat, Google Research and The Hebrew University of Jerusalem, Israel, Inbar Mosseri, Google Research, Oran Lang, Google Research, Tali Dekel, Google Research, Kevin Wilson, Google Research, Avinatan Hassidim, Google Research, William T. Freeman, Google Research, Michael Rubinstein, Google Research (2018)

LOSS FUNCTION WEIGHTING BASED ON SOURCE DOMINANCE FOR MONAURAL SOURCE SEPARATION USING RECURRENT NEURAL NETWORKS [PDF]

Seungtae Kang and Gil-Jin Jang, School of Electronics Engineering, Kyungpook National University, South Korea (2018)

LOW-ARTIFACT SOURCE SEPARATION USING PROBABILISTIC LATENT COMPONENT ANALYSIS [permalink]

Nasser Mohammadiha∗†, Paris Smaragdis‡, Arne Leijon†, † KTH Royal Institute of Technology, ‡University of Illinois, Adobe Systems (2013)

LOW-COMPLEXITY RECURSIVE-LEAST-SQUARES-BASED ONLINE NONNEGATIVE MATRIX FACTORIZATION ALGORITHM FOR AUDIO SOURCE SEPARATION [PDF]

Seokjin Lee, Dept. Electronic Eng., Kyonggi University, Kyonggi-Do, Republic of Korea (2017)

LOW-LATENCY BASS SEPARATION USING HARMONIC-PERCUSSION [PDF]

Ricard Marxer, Jordi Janer, Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain (2013)

LOW-LATENCY INSTRUMENT SEPARATION IN POLYPHONIC AUDIO USING TIMBRE MODELS [PDF]

Ricard Marxer, Jordi Janer, Jordi Bonada, Universitat Pompeu Fabra, Music Technology Group (2012)

LOW-LATENCY SOUND SOURCE SEPARATION USING DEEP NEURAL NETWORKS [permalink]

Gaurav Naithani, Giambattista Parascandolo, Tom Barker, Tuomas Virtanen, Tampere University of Technology, Department of Signal Processing, Tampere, Finland, Niels Henrik Pontoppidan, Eriksholm Research Centre, Oticon A/S, Snekkersten, Denmark (2016)

LOW-LATENCY SOUND-SOURCE-SEPARATION USING NON-NEGATIVE MATRIX FACTORISATION WITH COUPLED ANALYSIS AND SYNTHESIS DICTIONARIES [permalink]

Tom Barker, Tuomas Virtanen, Tampere University of Technology, Department of Signal Processing, Tampere, Finland, Niels Henrik Pontoppidan, Eriksholm Research Centre, Oticon A/S, Denmark (2015)

LOW-RANK REPRESENTATION OF BOTH SINGING VOICE AND MUSIC ACCOMPANIMENT VIA LEARNED DICTIONARIES [PDF]

Yi-Hsuan Yang, Research Center for IT Innovation, Academia Sinica, Taiwan (2013)

LYRICS-INFORMED SINGING VOICE SEPARATION (in Korean) [PDF]

전창빈, Graduate School of Convergence Science and Technology, Dept. of Intelligence and Information, Seoul National University (2021)