Digital Da Vinci

[1]  Juan Pablo Bello,et al.  On the Relative Importance of Individual Components of Chord Recognition Systems , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[2]  Shlomo Dubnov,et al.  Universal Prediction Applied to Stylistic Music Generation , 2002 .

[3]  Mark B. Sandler,et al.  Symbolic Representation of Musical Chords: A Proposed Syntax for Text Annotations , 2005, ISMIR.

[4]  James H. Watt,et al.  Entropy and Structure , 1977 .

[5]  Matthias M. Müller,et al.  Processing of affective pictures modulates right-hemispheric gamma band EEG activity , 1999, Clinical Neurophysiology.

[6]  Roger Shepard,et al.  Pitch perception and measurement , 1999 .

[7]  Gérard Assayag,et al.  New computational paradigms for computer music , 2009 .

[8]  Tijl De Bie,et al.  An End-to-End Machine Learning System for Harmonic Analysis of Music , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[9]  Gyuhwan Oh,et al.  A Study on New Gameplay Based on Brain-Computer Interface , 2009, DiGRA Conference.

[10]  Morwaread Farbood,et al.  Hyperscore : a new approach to interactive, computer-generated music , 2001 .

[11]  W. Schultz.  Behavioral theories and the neurophysiology of reward. , 2006, Annual review of psychology.

[12]  Stephen McAdams,et al.  Structural and affective aspects of music from statistical audio signal analysis , 2006, J. Assoc. Inf. Sci. Technol..

[13]  David Wessel,et al.  Timbre Space as a Musical Control Structure , 1979 .

[14]  François Pachet,et al.  The bag-of-frames approach to audio pattern recognition: a sufficient model for urban soundscapes but not for polyphonic music. , 2007, The Journal of the Acoustical Society of America.

[15]  Juan Pablo Bello,et al.  Audio-Based Cover Song Retrieval Using Approximate Chord Sequences: Testing Shifts, Gaps, Swaps and Beats , 2007, ISMIR.

[16]  Geoffroy Peeters,et al.  Large-Scale Study of Chord Estimation Algorithms Based on Chroma Representation and HMM , 2007, 2007 International Workshop on Content-Based Multimedia Indexing.

[17]  Matthias M. Müller,et al.  Effects of emotional arousal in the cerebral hemispheres: a study of oscillatory brain activity and event-related potentials , 2001, Clinical Neurophysiology.

[18]  Xavier Serra,et al.  Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[19]  John R. Smith,et al.  Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment , 2005, EURASIP J. Adv. Signal Process..

[20]  Darrell Conklin,et al.  Music Generation from Statistical Models , 2003 .

[21]  Agawu.  Trends in African Musicology: A Review Article , 2012 .

[22]  George Tzanetakis,et al.  An experimental comparison of audio tempo induction algorithms , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[23]  Daniel G. Brown,et al.  BLAST for Audio Sequences Alignment: A Fast Scalable Cover Identification Tool , 2012, ISMIR.

[24]  Leontios J. Hadjileontiadis,et al.  Adaptive Emotional Information Retrieval From EEG Signals in the Time-Frequency Domain , 2012, IEEE Transactions on Signal Processing.

[25]  Joan Serrà,et al.  Music Mood Representations from Social Tags , 2009, ISMIR.

[26]  Ben R. Newell,et al.  Universal aesthetic of fractals , 2003, Comput. Graph..

[27]  Jean-Julien Aucouturier,et al.  Ten Experiments on the Modeling of Polyphonic Timbre. (Dix Expériences sur la Modélisation du Timbre Polyphonique) , 2006 .

[28]  J. Russell.  A circumplex model of affect. , 1980 .

[29]  Ichiro Fujinaga,et al.  An Expert Ground Truth Set for Audio Chord Recognition and Music Analysis , 2011, ISMIR.

[30]  Jason M Haberman,et al.  Sensorimotor coupling in music and the psychology of the groove. , 2012, Journal of experimental psychology. General.

[31]  Lie Lu,et al.  Automatic mood detection and tracking of music audio signals , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[32]  R. Nagarajan,et al.  Appraising human emotions using Time Frequency Analysis based EEG alpha band features , 2009, 2009 Innovative Technologies in Intelligent Systems and Industrial Applications.

[33]  Peter Knees,et al.  On Rhythm and General Music Similarity , 2009, ISMIR.

[34]  Gerhard Widmer,et al.  Improving tempo-sensitive and tempo-robust descriptors for rhythmic similarity , 2011 .

[35]  P. Smaragdis,et al.  Non-negative matrix factorization for polyphonic music transcription , 2003, 2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (IEEE Cat. No.03TH8684).

[36]  Marc Leman,et al.  The Cortical Topography of Tonal Structures Underlying Western Music , 2002, Science.

[37]  Fred Lerdahl,et al.  Tonal Pitch Space , 2001 .

[38]  Elaine Chew,et al.  Visual feedback in performer-machine interaction for musical improvisation , 2007, NIME '07.

[39]  Max E. Valentinuzzi,et al.  Artifact removal from EEG signals using adaptive filters in cascade , 2007 .

[40]  Matthew E. P. Davies,et al.  Context-Dependent Beat Tracking of Musical Audio , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[41]  Christian Osendorfer,et al.  Music Similarity Estimation with the Mean-Covariance Restricted Boltzmann Machine , 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

[42]  James Pritchett,et al.  The Music of John Cage , 1993 .

[43]  Ron J. Weiss,et al.  Exploring common variations in state of the art chord recognition systems , 2010 .

[44]  Justin London,et al.  音楽のリズム に関する最近の神経科学的研究について : 『Hearing in Time』第二版､ 第三章｢神経生物学とリズムの発達｣より抜粋 , 2012 .

[45]  L. Trainor,et al.  Frontal brain electrical activity (EEG) distinguishes valence and intensity of musical emotions , 2001 .

[46]  Juhan Nam,et al.  Learning Sparse Feature Representations for Music Annotation and Retrieval , 2012, ISMIR.

[47]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[48]  Jeanne Bamberger,et al.  Developing musical intuitions : a project-based introduction to making and understanding music , 2000 .

[49]  R.W. Schafer,et al.  From frequency to quefrency: a history of the cepstrum , 2004, IEEE Signal Processing Magazine.

[50]  Jean Gotman,et al.  Automatic removal of eye movement artifacts from the EEG using ICA and the dipole model , 2009 .

[51]  Colin Potts,et al.  Design of Everyday Things , 1988 .

[52]  Juan Pablo Bello,et al.  Towards the automated analysis of simple polyphonic music: A knowledge-based approach (Ph.D. Thesis) , 2003 .

[53]  R. Andrzejak,et al.  Cross recurrence quantification for cover song identification , 2009 .

[54]  Camilo Rueda,et al.  Computer Assisted Composition at Ircam , 1999 .

[55]  Peter A. Hancock,et al.  Hedonomics: The Power of Positive and Pleasurable Ergonomics , 2005 .

[56]  Simon Dixon,et al.  Simultaneous Estimation of Chords and Musical Context From Audio , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[57]  Xavier Serra,et al.  Transmission Two: The Great Excursion (TT:TGE)—The Aesthetic, Art and Science of a Composition for Radio , 1991 .

[58]  Juan Pablo Bello,et al.  Non-Linear Semantic Embedding for Organizing Large Instrument Sample Libraries , 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

[59]  Yannis Stylianou,et al.  A scale transform based method for rhythmic similarity of music , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[60]  Yann LeCun,et al.  Unsupervised Learning of Sparse Features for Scalable Audio Classification , 2011, ISMIR.

[61]  Carlos Agon,et al.  OpenMusic 5: A Cross-Platform Release of the Computer-Assisted Composition Environment , 2005 .

[62]  Geoffroy Peeters.  Spectral and Temporal Periodicity Representations of Rhythm for the Automatic Classification of Music Audio Signal , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[63]  Miller S. Puckette.  A divide between 'compositional' and 'performative' aspects of Pd ⁄ , 2004 .

[64]  M. Pearce,et al.  Sweet Anticipation : Music and the Psychology of Expectation , 2007 .

[65]  T. Demiralp,et al.  Comparative analysis of event-related potentials during Go/NoGo and CPT: Decomposition of electrophysiological markers of response inhibition and sustained attention , 2006, Brain Research.

[66]  Shlomo Dubnov,et al.  OMax brothers: a dynamic yopology of agents for improvization learning , 2006, AMCMM '06.

[67]  Yoonseon Song,et al.  A time-frequency analysis of the EEG evoked by negative and positive visual stimuli , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[68]  Kyogu Lee,et al.  Identifying Cover Songs from Audio Using Harmonic Representation , 2006 .

[69]  Jonathan R Wolpaw,et al.  Brain–computer interface systems: progress and prospects , 2007, Expert review of medical devices.

[70]  Barry Vercoe,et al.  A Manual for the Audio Processing System and Supporting Programs with Tutorials , 2001 .

[71]  Mieczyslaw Kolinski,et al.  A Cross-Cultural Approach to Metro-Rhythmic Patterns , 1973 .

[72]  Henkjan Honing,et al.  Structure and Interpretation of Rhythm in Music , 2013 .

[73]  Maxime Crochemore,et al.  Factor Oracle: A New Structure for Pattern Matching , 1999, SOFSEM.

[74]  Richard J. Davidson,et al.  Now You Feel It, Now You Don't , 2003, Psychological science.

[75]  David Wessel,et al.  Analyzing Drum Patterns Using Conditional Deep Belief Networks , 2012, ISMIR.

[76]  Gert R. G. Lanckriet,et al.  Learning Content Similarity for Music Recommendation , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[77]  J. Pineda,et al.  Learning to control brain rhythms: making a brain-computer interface possible , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[78]  Shlomo Dubnov,et al.  Improvisation Planning and Jam Session Design using concepts of Sequence Variation and Flow Experience , 2005 .

[79]  Laurent Daudet,et al.  Sparse and structured decompositions of signals with the molecular matching pursuit , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[80]  Daniel P. W. Ellis,et al.  A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures , 2004, Computer Music Journal.

[81]  Dirk Heylen,et al.  Brain-Computer Interfacing and Games , 2010, Brain-Computer Interfaces.

[82]  Honglak Lee,et al.  Unsupervised feature learning for audio classification using convolutional deep belief networks , 2009, NIPS.

[83]  Juan Pablo Bello,et al.  Learning a robust Tonnetz-space transform for automatic chord recognition , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[84]  Ron J. Weiss,et al.  Unsupervised Discovery of Temporal Structure in Music , 2011, IEEE Journal of Selected Topics in Signal Processing.

[85]  Arnaud Lefebvre,et al.  Compror: Compression with a Factor Oracle , 2001, Data Compression Conference.

[86]  Daniel P. W. Ellis,et al.  Chord Recognition and Segmentation Using EM-trained Hidden Markov Models , 2003 .

[87]  François Pachet,et al.  The Continuator: Musical Interaction With Style , 2003, ICMC.

[88]  Ichiro Fujinaga,et al.  A Cross-Validated Study of Modelling Strategies for Automatic Chord Recognition in Audio , 2007, ISMIR.

[89]  Elaine Chew,et al.  Performer-centered visual feedback for human-machine improvisation , 2011, CIE.

[90]  Simon Dixon,et al.  Approximate Note Transcription for the Improved Identification of Difficult Chords , 2010, ISMIR.

[91]  Wolfgang E. Kuhn,et al.  Computer-Assisted Teaching: A New Approach to Research in Music , 1967 .

[92]  Youngmoo E. Kim,et al.  Learning emotion-based acoustic features with deep belief networks , 2011, 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).

[93]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[94]  Douglas Eck,et al.  Learning Features from Music Audio with Deep Belief Networks , 2010, ISMIR.

[95]  D. Ruelle,et al.  Recurrence Plots of Dynamical Systems , 1987 .

[96]  Pedro Guerra,et al.  [The International Affective Digitized Sounds (IADS): Spanish norms]. , 2008, Psicothema.

[97]  Anssi Klapuri,et al.  Sound onset detection by applying psychoacoustic knowledge , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[98]  Belinda Thom,et al.  Interactive Improvisational Music Companionship: A User-Modeling Approach , 2003, User Modeling and User-Adapted Interaction.

[99]  Ursula Dresdner,et al.  Music Cognition And Computerized Sound An Introduction To Psychoacoustics , 2016 .

[100]  Markus Schedl,et al.  Local and global scaling reduce hubs in space , 2012, J. Mach. Learn. Res..

[101]  Thierry Pun,et al.  A channel selection method for EEG classification in emotion assessment based on synchronization likelihood , 2007, 2007 15th European Signal Processing Conference.

[102]  George E. Lewis.  Too Many Notes: Computers, Complexity and Culture in Voyager , 2000, Leonardo Music Journal.

[103]  Gert R. G. Lanckriet,et al.  Semantic Annotation and Retrieval of Music and Sound Effects , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[104]  Godfried T. Toussaint.  The Geometry of Musical Rhythm: What Makes a "Good" Rhythm Good? , 2013 .

[105]  Belinda Thom,et al.  Unsupervised Learning and Interactive Jazz/Blues Improvisation , 2000, AAAI/IAAI.

[106]  Judith C. Brown.  Calculation of a constant Q spectral transform , 1991 .

[107]  Thierry Bertin-Mahieux,et al.  The Million Song Dataset , 2011, ISMIR.

[108]  Mark B. Sandler,et al.  Automatic Rhythm Modification of Drum Loops , 2007, IEEE Signal Processing Letters.

[109]  Maurizio Omologo,et al.  Use of Hidden Markov Models and Factored Language Models for Automatic Chord Recognition , 2009, ISMIR.

[110]  Anssi Klapuri,et al.  Automatic Classification of Pitched Musical Instrument Sounds , 2006 .

[111]  William F. Walker,et al.  A computer participant in musical improvisation , 1997, CHI.

[112]  Nathan A. Fox,et al.  Conceptual, biological, and behavioral distinctions among different categories of shy children. , 1999 .

[113]  Thierry Bertin-Mahieux,et al.  Large-Scale Cover Song Recognition Using the 2D Fourier Transform Magnitude , 2012, ISMIR.

[114]  Shlomo Dubnov,et al.  Using Factor Oracles for Machine Improvisation , 2004, Soft Comput..

[115]  Peter Grosche,et al.  Extracting Predominant Local Pulse Information From Music Recordings , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[116]  C. Krumhansl.  Cognitive Foundations of Musical Pitch , 1990 .

[117]  Geraint A. Wiggins,et al.  Towards Greater Objectivity in Music Theory: Information-Dynamic Analysis of Minimalist Music , 2007 .

[118]  Juan Pablo Bello,et al.  A Feature Smoothing Method for Chord Recognition Using Recurrence Plots , 2011, ISMIR.

[119]  Daniel P. W. Ellis,et al.  Structured Prediction Models for Chord Transcription of Music Audio , 2009, 2009 International Conference on Machine Learning and Applications.

[120]  Enzo Pasquale Scilingo,et al.  The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition , 2012, IEEE Transactions on Affective Computing.