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This paper presents the Jazz Transformer, a generative model that utilizes a neural sequence model called the Transformer-XL for modeling lead sheets of Jazz music.
A mathematical theory of communication
Claude E Shannon · 1948
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Visualizing music and audio using self-similarity
Jonathan Foote · 1999
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Melodic improvisation on a twelve bar blues model: an investigation of physical and historical aspects and their contribution to performance
Simon John Nelson · 2001
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How Jazz musicians improvise
Philip Johnson-Laird · 2002
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This is Your Brain on Music: The Science of a Human Obsession
Daniel J. Levitin · 2006
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A segment-based fitness measure for capturing repetitive structures of music recordings
Meinard Müller, Peter Grosche, and Nanzhu Jiang · 2011
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A scape plot representation for visualizing repetitive structures of music recordings
Meinard Müller and Nanzhu Jiang · 2012
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Introducing the Jazzomat project – Jazz solo analysis using music information retrieval methods
Jakob Abeßer, Klaus Frieler, Martin Pfleiderer, and Wolf-Georg Zaddach · 2013
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The generative electronic dance music algorithmic system (GEDMAS)
Christopher Anderson, Arne Eigenfeldt, and Philippe Pasquier · 2013
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SM Toolbox: MATLAB implementations for computing and enhancing similarity matrices
Meinard Müller, Nanzhu Jiang, and Harald G. Grohganz · 2014
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Score-informed analysis of intonation and pitch modulation in Jazz solos
Jakob Abeßer, Estefanía Cano, Klaus Frieler, Martin Pfleiderer, and Wolf-Georg Zaddach · 2015
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Midlevel analysis of monophonic Jazz solos: A new approach to the study of improvisation
Klaus Frieler, Martin Pfleiderer, Wolf-Georg Zaddach, and Jakob Abeßer · 2016
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Phrase-level audio segmentation of jazz improvisations informed by symbolic data
Jeff Gregorio and Youngmoo Kim · 2016
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Automatic melodic reduction using a supervised probabilistic context-free grammar
Ryan Groves · 2016
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Deep learning for Jazz walking bass transcription
Jakob Abeßer, Stefan Balke, Klaus Frieler, Martin Pfleiderer, and Meinard Müller · 2017
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Chord generation from symbolic melody using BLSTM networks
Hyungui Lim, Seungyeon Rhyu, and Kyogu Lee · 2017
Cited alongside, same era.
Inside the Jazzomat — New Perspectives for Jazz Research
Martin Pfleiderer, Klaus Frieler, Jakob Abeßer, Wolf-Georg Zaddach, and Benjamin Burkhart, editors · 2017
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Taking the models back to music practice: Evaluating generative transcription models built using deep learning
Bob L. Sturm and Oded Ben-Tal · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Modeling temporal tonal relations in polyphonic music through deep networks with a novel image-based representation
Ching-Hua Chuan and Dorien Herremans · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems
Jean-Pierre Briot, Gaëtan Hadjeres, and François Pachet · 2019
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Harmony Transformer: Incorporating chord segmentation into harmony recognition
Tsung-Ping Chen and Li Su · 2019
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Transformer-XL: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, and Ruslan Salakhutdinov · 2019
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LakhNES: Improving multi-instrumental music generation with cross-domain pre-training
Chris Donahue, Huanru Henry Mao, Yiting Ethan Li, Garrison W. Cottrell, and Julian McAuley · 2019
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Music Transformer: Generating music with long-term structure
Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Ian Simon, Curtis Hawthorne, Noam Shazeer, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, and Douglas Eck · 2019
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment
Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, and Yi-Hsuan Yang · 2018
Cited alongside, same era.
Audio-aligned Jazz harmony dataset for automatic chord transcription and corpus-based research
Vsevolod Eremenko, Emir Demirel, Baris Bozkurt, and Xavier Serra · 2018
Cited alongside, same era.
Lead sheet generation and arrangement via a hybrid generative model
Hao-Min Liu, Meng-Hsuan Wu, and Yi-Hsuan Yang · 2018
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StructureNet: Inducing structure in generated melodies
Gabriele Medeot, Srikanth Cherla, Katerina Kosta, Matt McVicar, Samer Abdallah, Marco Selvi, Ed Newton-Rex, and Kevin Webster · 2018
Cited alongside, same era.
JazzGAN: Improvising with generative adversarial networks
Nicholas Trieu and Robert M. Keller · 2018
Cited alongside, same era.
Computational corpus analysis: A case study on Jazz solos
Christof Weiss, Stefan Balke, Jakob Abeßer, and Meinard Müller · 2018
Cited alongside, same era.
Improving automatic jazz melody generation by transfer learning techniques
Hsiao-Tzu Hung, Chung-Yang Wang, Yi-Hsuan Yang, and Hsin-Min Wang · 2019
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Algorithmic ability to predict the musical future: Datasets and evaluation
Berit Janssen, Tom Collins, and Iris Ren · 2019
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Modeling self-repetition in music generation using structured adversaries
Harsh Jhamtani and Taylor Berg-Kirkpatrick · 2019
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A comparative study on Transformer vs RNN in speech applications
Shigeki Karita et al · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Rhythm, chord and melody generation for lead sheets using recurrent neural networks
Cedric De Boom, Stephanie Van Laere, Tim Verbelen, and Bart Dhoedt · 2020
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Pop Music Transformer: Beat-based modeling and generation of expressive Pop piano compositions
Yu-Siang Huang and Yi-Hsuan Yang · 2020
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Transformers are RNNs: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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Music generation with temporal structure augmentation
Shakeel Raja · 2020
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