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Research on generative systems in music has seen considerable attention and growth in recent years.
A Style-Specific Music Composition Neural Network
C. Jin, Y. Tie, Y. Bai, X. Lv, and S. Liu. 2020 · 1912
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A technique for the measurement of attitudes
R. Likert. 1932 · 1932
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The Philosophy of PCM
B.M. Oliver, J.R. Pierce, and C.E. Shannon. 1948 · 1948
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Experimental Music: Composition with an Electronic Computer
L. A. Hiller and L. M. Isaacson. 1959 · 1959
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Human behavior and the principle of least effort; an introduction to human ecology
G. K. Zipf. 1965 · 1965
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Aesthetics and psychobiology
D. E. Berlyne. 1971 · 1971
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A new auditory model for the evaluation of sound quality of audio systems. In Proc. ICASSP , Vol. 10. IEEE, Tampa
M. Karjalainen. 1985 · 1985
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Flow: The Psychology of Optimal Experience
M. Csíkszentmihályi. 1990 · 1990
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Pitch structure
W. Jay Dowling. 1991 · 1991
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Usability Engineering
J. Nielsen. 1994 · 1994
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P.800 : Methods for subjective determination of transmission quality
ITU-T. 1996 · 1996
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Creativity versus Determinism: Cognitive Science and Music Theory as Touchstones of Automatic Music Composition. In Proc. ICMC . ICMA, Hong Kong
D. Zimmermann. 1996 · 1996
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‘Improving ratings’: audit in the British University system
M. Strathern. 1997 · 1997
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Creativity and artificial intelligence
M. A. Boden. 1998 · 1998
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A Cognitive Dimensions Questionnaire Optimised for Users. In Proc. Workshop on Philosophy of Programming Interest Group (PPIG)
A. F. Blackwell and T. R. G. Green. 2000 · 2000
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The algorithmic composer
D. Cope. 2000 · 2000
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Tonality Induction: A Statistical Approach Applied Cross-Culturally
C. L. Krumhansl. 2000 · 2000
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MusicXML: An Internet-Friendly Format for Sheet Music. In Proc. XML Conf. Orlando
M. Good. 2001 · 2001
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A Few Remarks on Algorithmic Composition
M. Supper. 2001 · 2001
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Evaluation of Input Devices for Musical Expression: Borrowing Tools from HCI
M. Wanderley and N. Orio. 2002 · 2002
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Evolutionary Music and the Zipf-Mandelbrot Law: Developing Fitness Functions for Pleasant Music. In Applic. of Evolutionary Comput. (Lecture Notes in Computer Science) . Springer, Berlin, 522–534
B. Manaris, D. Vaughan, C. Wagner, J. Romero, and R. B. Davis. 2003 · 2003
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Funology: From Usability to Enjoyment (1 ed.)
M. A. Blythe, K. Overbeeke, A. F. Monk, and P. C. Wright. 2004 · 2004
Earlier work this paper cites.
General perspectives on achieving musical excellence
R. Chaffin and A. F. Lemieux. 2004 · 2004
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Musical Qualia, Context, Time and Emotion
J. Goguen. 2004 · 2004
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The cognition of basic musical structures (1. paperback ed ed.)
D. Temperley. 2004 · 2004
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Computer Models of Musical Creativity
D. Cope. 2005 · 2005
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Jukebox: A Generative Model for Music
P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford, and I. Sutskever. 2020 · 2005
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Developing Fitness Functions for Pleasant Music: Zipf’s Law and Interactive Evolution Systems. In Applic. of Evolutionary Comput. (Lecture Notes in Computer Science) . Springer, Berlin, 498–507
B. Manaris, P. Machado, C. McCauley, J. Romero, and D. Krehbiel. 2005 · 2005
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Music Plagiarism Detection Using Melody Databases. In Proc. KES , Vol. 3683. Springer Berlin Heidelberg, Berlin
J.-I. Park, S.-W. Kim, and M. Shin. 2005 · 2005
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Performance measurement in blind audio source separation
E. Vincent, R. Gribonval, and C. Fevotte. 2006 · 2005
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Practice-Led Strategies for Interactive Art Research
L. Candy, S. Amitani, and Z. Bilda. 2006 · 2006
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Usability Evaluation for Mobile Device: A Comparison of Laboratory and Field Tests. In Proc. MobileHCI . ACM, Helsinki
H. B.-L. Duh, G. C. B. Tan, and V. H.-H. Chen. 2006 · 2006
Earlier work this paper cites.
Nasa-Task Load Index (NASA-TLX); 20 Years Later
S. G. Hart. 2006 · 2006
Earlier work this paper cites.
Sweet Anticipation: Music and the Psychology of Expectation
D. Huron. 2006 · 2006
Earlier work this paper cites.
BS.1387:2006: Method for objective measurements of perceived audio quality
ITU-R. 2006 · 2006
Earlier work this paper cites.
Modeling Perceptual Similarity of Audio Signals for Blind Source Separation Evaluation. In Proc. ICA . Springer, Berlin
B. Fox, A. Sabin, B. Pardo, and A. Zopf. 2007 · 2007
Earlier work this paper cites.
Some Empirical Criteria for Attributing Creativity to a Computer Program
G. Ritchie. 2007 · 2007
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The Need for Open Source Software in Machine Learning. In Proc. Conf. on Human-Computer Interaction with Mobile Devices and Services
S. Sonnenburg, M. L. Braun, C. S. Ong, S. Bengio, L. Bottou, G. Holmes, and Y. LeCun. 2007 · 2007
Earlier work this paper cites.
An Empirical Evaluation of the System Usability Scale
A. Bangor, P. T. Kortum, and J. T. Miller. 2008 · 2008
Earlier work this paper cites.
The Analysis of Generative Music Programs
N. Collins. 2008 · 2008
Earlier work this paper cites.
Construction and Evaluation of a User Experience Questionnaire. In Proc. Symp. on HCI and Usability for Education and Work . Springer, Berlin
B. Laugwitz, T. Held, and M. Schrepp. 2008 · 2008
Earlier work this paper cites.
Subjective Appraisal of Music
E. Brattico and T. Jacobsen. 2009 · 2009
Earlier work this paper cites.
Software-Based Extraction of Objective Parameters from Music Performances
A. Lerch. 2009 · 2009
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Court decisions on music plagiarism and the predictive value of similarity algorithms
D. Müllensiefen and M. Pendzich. 2009 · 2009
Earlier work this paper cites.
Reproducible research in signal processing
P. Vandewalle, J. Kovacevic, and M. Vetterli. 2009 · 2009
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Toward Understanding Human-Computer Interaction in Composing the Instrument. In Proc. ICMC . ICMA
R. Fiebrink, D. Trueman, C. Britt, M. Nagai, K. Kaczmarek, M. Early, M. R. Daniel, A. Hege, and P. Cook. 2010 · 2010
Earlier work this paper cites.
Experimental aesthetics and liking for music
David Hargreaves. 2010 · 2010
Earlier work this paper cites.
Assessment of Creativity
J. A. Plucker and M. C. Makel. 2010 · 2010
Earlier work this paper cites.
Subjective and Objective Quality Assessment of Audio Source Separation
V. Emiya, E. Vincent, N. Harlander, and V. Hohmann. 2011 · 2011
Earlier work this paper cites.
Human Model Evaluation in Interactive Supervised Learning. In Proc. CHI . ACM, Vancouver
R. Fiebrink, P. R. Cook, and D. Trueman. 2011 · 2011
Earlier work this paper cites.
Measure Twice, Cut down Error: A Process for Enhancing the Validity of Survey Scales
H. Gehlbach and M. E. Brinkworth. 2011 · 2011
Earlier work this paper cites.
On Impact and Evaluation in Computational Creativity: A Discussion of the Turing Test and an Alternative Proposal. In Proc. AISB
A. Pease and S. Colton. 2011 · 2011
Earlier work this paper cites.
A Geometry of Music: Harmony and Counterpoint in the Extended Common Practice
D. Tymoczko. 2011 · 2011
Earlier work this paper cites.
The perception of similarity in court cases of melodic plagiarism and a review of measures of melodic similarity. In Proc. SysMus . Cologne
A. Wolf and D. Müllensiefen. 2011 · 2011
Earlier work this paper cites.
Singing from the same sheet: computational melodic similarity measurement and copyright law
R. J. S. Cason and D. Müllensiefen. 2012 · 2012
Earlier work this paper cites.
Research in the Wild: Understanding ‘In the Wild’ Approaches to Design and Development. In Proc. DIS . ACM, New York
A. Chamberlain, A. Crabtree, T. Rodden, M. Jones, and Y. Rogers. 2012 · 2012
Earlier work this paper cites.
Plagiarism detection in polyphonic music using monaural signal separation. In Proc. INTERSPEECH . ISCA, Portland
S. De, I. Roy, T. Prabhakar, K. Suneja, S. Chaudhuri, R. Singh, and B. Raj. 2012 · 2012
Earlier work this paper cites.
Computational Aesthetic Evaluation: Past and Future
P. Galanter. 2012 · 2012
Earlier work this paper cites.
A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola. 2012 · 2012
Earlier work this paper cites.
A Standardised Procedure for Evaluating Creative Systems: Computational Creativity Evaluation Based on What it is to be Creative
A. Jordanous. 2012 · 2012
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On Evaluating Systems for Generating Expressive Music Performance: the Rencon Experience
H. Katayose, M. Hashida, G. De Poli, and K. Hirata. 2012 · 2012
Earlier work this paper cites.
Performance-Led Research in the Wild
S. Benford, C. Greenhalgh, A. Crabtree, M. Flintham, B. Walker, J. Marshall, B. Koleva, S. Rennick Egglestone, G. Giannachi, M. Adams, N. Tandavanitj, and J. Row Farr. 2013 · 2013
Earlier work this paper cites.
Successful Qualitative Research: A Practical Guide for Beginners
V. Braun and V. Clarke. 2013 · 2013
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AI Methods in Algorithmic Composition: A Comprehensive Survey
J. D. Fernandez and F. Vico. 2013 · 2013
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From everyday emotions to aesthetic emotions: Towards a unified theory of musical emotions
P. N. Juslin. 2013 · 2013
Earlier work this paper cites.
The Flow Grid: A Technique for Observing and Measuring Emotional State in Children Interacting with a Flow Machine
A. R. Addessi, L. Ferrari, and F. Carugati. 2015 · 2014
Earlier work this paper cites.
Quantifying the Creativity Support of Digital Tools through the Creativity Support Index
E. Cherry and C. Latulipe. 2014 · 2014
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The Musicality of Non-Musicians: An Index for Assessing Musical Sophistication in the General Population
D. Müllensiefen, B. Gingras, J. Musil, and L. Stewart. 2014 · 2014
Earlier work this paper cites.
When To Ask Participants To Think Aloud: A Comparative Study of Concurrent and Retrospective Think-Aloud Methods
T. Alshammari, O. Alhadreti, and P. Mayhew. 2015 · 2015
Earlier work this paper cites.
Third-Wave HCI, 10 Years Later—Participation and Sharing
S. Bødker. 2015 · 2015
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Goodhart’s Law
C. Goodhart. 2015 · 2015
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On the Perceptual Relevance of Objective Source Separation Measures for Singing Voice Separation. In Proc. WASPAA . IEEE, New Paltz
U. Gupta, E. Moore II, and A. Lerch. 2015 · 2015
Earlier work this paper cites.
ViSQOL: an objective speech quality model
A. Hines, J. Skoglund, A. C. Kokaram, and N. Harte. 2015 · 2015
Earlier work this paper cites.
Effects of Expertise on the Cognitive and Neural Processes Involved in Musical Appreciation
M. T. Pearce. 2015 · 2015
Earlier work this paper cites.
Interaction Design: Beyond Human-Computer Interaction
J. Preece, H. Sharp, and Y. Rogers. 2015 · 2015
Earlier work this paper cites.
Comparison of time series similarity measures for plagiarism detection in music. In Proc. IEEE India Conf. (INDICON) . IEEE
K. Suneja and M. Bansal. 2015 · 2015
Earlier work this paper cites.
Evaluation of Musical Creativity and Musical Metacreation Systems
K. Agres, J. Forth, and G. A. Wiggins. 2016 · 2016
Earlier work this paper cites.
Song From PI: A Musically Plausible Network for Pop Music Generation. In Proc. ICLR . San Juan
H. Chu, R. Urtasun, and S. Fidler. 2016 · 2016
Earlier work this paper cites.
Computational Music Analysis
D. Meredith (Ed.). 2016 · 2016
Earlier work this paper cites.
C-RNN-GAN: Continuous recurrent neural networks with adversarial training. In Proc. Constructive Machine Learning Workshop, NIPS
O. Mogren. 2016 · 2016
Earlier work this paper cites.
The Definition of User Experience (UX)
D. Norman and J. Nielsen. 2016 · 2016
Earlier work this paper cites.
Effects of Familiarity, Key Membership, and Interval Size on Perceiving Wrong Notes in Melodies. In Proc. ICMPC . San Francisco
R. Raman, K. Herndon, and W. J. Dowling. 2016 · 2016
Earlier work this paper cites.
Improved Techniques for Training GANs. In Proc. NeurIPS . Barcelona
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, and X. Chen. 2016 · 2016
Earlier work this paper cites.
A note on the evaluation of generative models. In Proc. ICLR . San Juan
L. Theis, A. v. d. Oord, and M. Bethge. 2016 · 2016
Earlier work this paper cites.
Music Plagiarism at a Glance: Metrics of Similarity and Visualizations. In Proc. IV
R. De Prisco, A. Esposito, N. Lettieri, D. Malandrino, D. Pirozzi, G. Zaccagnino, and R. Zaccagnino. 2017a · 2017
Earlier work this paper cites.
A computational intelligence text-based detection system of music plagiarism. In Proc. ICSAI
R. De Prisco, D. Malandrino, G. Zaccagnino, and R. Zaccagnino. 2017b · 2017
Earlier work this paper cites.
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders. In Proc. ICML . PMLR, Sydney
J. Engel, C. Resnick, A. Roberts, S. Dieleman, M. Norouzi, D. Eck, and K. Simonyan. 2017 · 2017
Earlier work this paper cites.
Automatic Sample Detection in Polyphonic Music. In Proc. ISMIR . Suzhou
S. Gururani and A. Lerch. 2017 · 2017
Earlier work this paper cites.
DeepBach: a Steerable Model for Bach Chorales Generation. In Proc. ICML . PMLR, Sydney
G. Hadjeres, F. Pachet, and F. Nielsen. 2017 · 2017
Earlier work this paper cites.
MorpheuS: Generating Structured Music with Constrained Patterns and Tension
D. Herremans and E. Chew. 2019 · 2017
Earlier work this paper cites.
A Functional Taxonomy of Music Generation Systems
D. Herremans, C.-H. Chuan, and E. Chew. 2017 · 2017
Cited alongside, same era.
CNN Architectures for Large-Scale Audio Classification. In Proc. ICASSP . IEEE, New Orleans
S. Hershey, S. Chaudhuri, D. P. W. Ellis, J. F. Gemmeke, A. Jansen, R. C. Moore, M. Plakal, D. Platt, R. A. Saurous, B. Seybold, M. Slaney, R. J. Weiss, and K. Wilson. 2017 · 2017
Cited alongside, same era.
Limitations from Assumptions in Generative Music Evaluation
M. O’Neill and R. Loughran. 2017 · 2017
Cited alongside, same era.
An Introduction to Musical Metacreation
P. Pasquier, A. Eigenfeldt, O. Bown, and S. Dubnov. 2017 · 2017
Cited alongside, same era.
Taking the Models back to Music Practice: Evaluating Generative Transcription Models built using Deep Learning
B. L. T. Sturm and O. Ben-Tal. 2017 · 2017
Cited alongside, same era.
Choosers: A Visual Programming Language for Nondeterministic Music Composition by Non-Programmers
M. Bellingham. 2022 · 2022
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"Explorers of Unknown Planets": Practices and Politics of Artificial Intelligence in Visual Arts. In Proc. ACMHCI , Vol. 6
B. Caramiaux and S. Fdili Alaoui. 2022 · 2022
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An Empirical Study on How People Perceive AI-generated Music. In Proc. CIKM . ACM, Atlanta
H. Chu, J. Kim, S. Kim, H. Lim, H. Lee, S. Jin, J. Lee, T. Kim, and S. Ko. 2022 · 2022
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A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends
M. Civit, J. Civit-Masot, F. Cuadrado, and M. J. Escalona. 2022 · 2022
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What is missing in deep music generation? A study of repetition and structure in popular music. In Proc. ISMIR . Bengaluru
S. Dai, H. Yu, and R. B. Dannenberg. 2022 · 2022
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L.-C. Yang, S.-Y. Chou, and Y.-H. Y. 2017 · 2017
Cited alongside, same era.
Demystifying MMD GANs. In Proc. ICLR . Vancouver
M. Binkowski, D. J. Sutherland, M. Arbel, and A. Gretton. 2018 · 2018
Cited alongside, same era.
Symbolic Music Genre Transfer with CycleGAN. In Proc. ICTAI
G. Brunner, Y. Wang, R. Wattenhofer, and S. Zhao. 2018 · 2018
Cited alongside, same era.
Practice-Based Research in the Creative Arts: Foundations and Futures from the Front Line
L. Candy and E. Edmonds. 2018 · 2018
Cited alongside, same era.
The challenge of realistic music generation: modelling raw audio at scale. In Proc. NeurIPS , Vol. 31. Barcelona
S. Dieleman, A. van den Oord, and K. Simonyan. 2018 · 2018
Cited alongside, same era.
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment. In Proc. AAAI , Vol. 32
H.-W. Dong, W.-Y. Hsiao, L.-C. Yang, and Y.-H. Yang. 2018 · 2018
Cited alongside, same era.
Computational Music Aesthetics: a survey and some thoughts. In Proc. CSMC . Dublin
S. Kalonaris and A. Jordanous. 2018 · 2018
Cited alongside, same era.
Speculating on Reflection and People’s Music Co-Creation with AI. In Proc. of Gen. AI and HCI Workshop, CHI . ACM
C. Ford and N. Bryan-Kinns. 2022 · 2022
Later among the works it cites.
Interrogating AI Bias through Digital Art
N. R. Gaskins. 2022 · 2022
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Subjective Evaluation of Deep Learning Models for Symbolic Music Composition. In Workshop on Gen. AI and HCI at CHI . ACM
C. Hernandez-Olivan, J. A. Puyuelo, and J. R. Beltran. 2022 · 2022
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Are Large Pre-Trained Language Models Leaking Your Personal Information?. In Findings Assoc. for Computational Linguistics (EMNLP) . Abu Dhabi
J. Huang, H. Shao, and K. C.-C. Chang. 2022 · 2022
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Changing the nature of AI research
S. Kambhampati. 2022 · 2022
Later among the works it cites.
Towards Evaluation of Autonomously Generated Musical Compositions: A Comprehensive Survey
D. Kvak. 2022 · 2022
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Expressive Communication: Evaluating Developments in Generative Models and Steering Interfaces for Music Creation. In Proc. IUI . ACM, Helsinki
R. Louie, J. Engel, and C.-Z. A. Huang. 2022 · 2022
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A Proposal to Compare the Similarity Between Musical Products. One More Step for Automated Plagiarism Detection?. In Mathematics and Computation in Music . Springer, Cham, 192–204
A. López-García, B. Martínez-Rodríguez, and V. Liern. 2022 · 2022
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An adaptive meta-heuristic for music plagiarism detection based on text similarity and clustering
D. Malandrino, R. De Prisco, M. Ianulardo, and R. Zaccagnino. 2022 · 2022
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Audio Similarity is Unreliable as a Proxy for Audio Quality. In Proc. INTERSPEECH . ISCA, Incheon
P. Manocha, Z. Jin, and A. Finkelstein. 2022 · 2022
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Pop Music Generation with Controllable Phrase Lengths. In Proc. ISMIR . Bengaluru
D. Naruse, T. Takahata, Y. Mukuta, and T. Harada. 2022 · 2022
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Six Human-Centered Artificial Intelligence Grand Challenges
O. Ozmen Garibay, B. Winslow, S. Andolina, M. Antona, A. Bodenschatz, C. Coursaris, G. Falco, S. M. Fiore, I. Garibay, K. Grieman, J. C. Havens, M. Jirotka, H. Kacorri, W. Karwowski, J. Kider, J. Konstan, S. Koon, M. Lopez-Gonzalez, I. Maifeld-Carucci, S. McGregor, G. Salvendy, B. Shneiderman, C. Stephanidis, C. Strobel, C. Ten Holter, and W. Xu. 2023 · 2022
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Music Plagiarism Detection Based on Siamese CNN
K. Park, S. Baek, J. Jeon, and Y.-S. Jeong. 2022 · 2022
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Musika! fast infinite waveform music generation. In Proc. ISMIR . Bengaluru
M. Pasini and J. Schlüter. 2022 · 2022
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On Creativity, Music’s AI Completeness, and Four Challenges for Artificial Musical Creativity
M. Rohrmeier. 2022 · 2022
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Human-Centered AI
B. Shneiderman. 2022 · 2022
Later among the works it cites.
Generative AI Helps One Express Things for Which They May Not Have Expressions (Yet). In Proc. of Gen. AI and HCI Workshop, CHI . ACM
B. Sturm. 2022 · 2022
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How Do Musicians Experience Jamming With a Co-Creative “AI”?. In Proc. NeurIPS. New Orleans
N. J. W. Thelle and R. Fiebrink. 2022 · 2022
Later among the works it cites.
Evaluating Generative Audio Systems and their Metrics. In Proc. ISMIR . Bangalore
A. Vinay and A. Lerch. 2022 · 2022
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MusicLM: Generating Music From Text
A. Agostinelli, T. I. Denk, Z. Borsos, J. Engel, M. Verzetti, A. Caillon, Q. Huang, A. Jansen, A. Roberts, M. Tagliasacchi, M. Sharifi, N. Zeghidour, and C. Frank. 2023 · 2023
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MUSIB: musical score inpainting benchmark
M. Araneda-Hernandez, F. Bravo-Marquez, D. Parra, and R. F. Cádiz. 2023 · 2023
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The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges
A. Bandi, P. V. S. R. Adapa, and Y. E. V. P. K. Kuchi. 2023 · 2023
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The Ethical Implications of Generative Audio Models: A Systematic Literature Review. In Proc. AAAI/ACM Conf. on AI, Ethics, and Soc. (AIES) . ACM, New York
J. Barnett. 2023 · 2023
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Transparency in Music-Generative AI: A Systematic Literature Review
R. Batlle-Roca, E. Gómez, W. Liao, X. Serra, and Y. Mitsufuji. 2023 · 2023
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Evaluating Human-AI Interaction via Usability, User Experience and Acceptance Measures for MMM-C: A Creative AI System for Music Composition. In Proc. IJCAI . Macao
R. Bougueng Tchemeube, J. Ens, C. Plut, P. Pasquier, M. Safi, Y. Grabit, and J.-B. Rolland. 2023 · 2023
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A Guide to Evaluating the Experience of Media and Arts Technology
N. Bryan-Kinns and C. N. Reed. 2023 · 2023
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Extracting Training Data from Diffusion Models. In Proc. USENIX Security Symp. (USENIX Security)
N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tramèr, B. Balle, D. Ippolito, and E. Wallace. 2023 · 2023
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Simple and Controllable Music Generation. In Proc. NeurIPS , Vol. 36. New Orleans
J. Copet, F. Kreuk, I. Gat, T. Remez, D. Kant, G. Synnaeve, Y. Adi, and A. Defossez. 2023 · 2023
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Is Quality Enough? Integrating Energy Consumption in a Large-Scale Evaluation of Neural Audio Synthesis Models. In Proc. ICASSP . IEEE, Rhodes
C. Douwes, G. Bindi, A. Caillon, P. Esling, and J.-P. Briot. 2023 · 2023
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High Fidelity Neural Audio Compression
A. Défossez, J. Copet, G. Synnaeve, and Y. Adi. 2023 · 2023
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Art and the science of generative AI
Z. Epstein, A. Hertzmann, and The Investigators of Human Creativity. 2023 · 2023
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Towards a Reflection in Creative Experience Questionnaire. In Proc. CHI . ACM, Hamburg
C. Ford and N. Bryan-Kinns. 2023 · 2023
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An automatic music generation and evaluation method based on transfer learning
Y. Guo, Y. Liu, T. Zhou, L. Xu, and Q. Zhang. 2023 · 2023
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Rethinking FID: Towards a Better Evaluation Metric for Image Generation. In Proc. CVPR . Seattle
S. Jayasumana, S. Ramalingam, A. Veit, D. Glasner, A. Chakrabarti, and S. Kumar. 2023 · 2023
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A Survey on Deep Learning for Symbolic Music Generation: Representations, Algorithms, Evaluations, and Challenges
S. Ji, X. Yang, and J. Luo. 2023 · 2023
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Aesthetic judgments of music: Reliability, consistency, criteria, self-insight, and expertise
P. N. Juslin, E. Ingmar, and J. Danielsson. 2023 · 2023
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Efficient Neural Music Generation. In Proc. NeurIPS , Vol. 36. New Orleans
M. W. Y. Lam, Q. Tian, T. Li, Z. Yin, S. Feng, M. Tu, Y. Ji, R. Xia, M. Ma, X. Song, J. Chen, W. Yuping, and Y. Wang. 2023 · 2023
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An Introduction to Audio Content Analysis: Music Information Retrieval Tasks and Applic. (2 ed.)
A. Lerch. 2023 · 2023
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Harmonizing minds and machines: survey on transformative power of machine learning in music
J. Liang. 2023 · 2023
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AudioLDM: Text-to-audio Generation with Latent Diffusion Models
H. Liu, Z. Chen, Y. Yuan, X. Mei, X. Liu, D. Mandic, W. Wang, and M. D. Plumbley. 2023 · 2023
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Imaginings from an unfamiliar world: Narrative engagement with a new musical system
P. Loui, B. M. Kubit, Y. Ou, and E. H. Margulis. 2023 · 2023
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An Autoethnographic Exploration of XAI in Algorithmic Composition. In Proc. Int. Workshop on Explainable AI for the Arts (XAIxArts) at ACM Creativity and Cognition
A. Noel-Hirst and N. Bryan-Kinns. 2023 · 2023
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Responsible AI and the Arts: The Ethical and Legal Implications of AI in the Arts and Creative Industries. In Proc. Int. Symp. on Trustworthy Autonomous Systems (TAS) . ACM, Edinburgh
A. M. Piskopani, A. Chamberlain, and C. Ten Holter. 2023 · 2023
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AI composer bias: Listeners like music less when they think it was composed by an AI
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