Fetching the paper…
Reading the bibliography…
To make music composition more approachable, we designed the first AI-powered Google Doodle, the Bach Doodle, where users can create their own melody and have it harmonized by a machine learning model Coconet (Huang et al., 2017) in the style of Bach.
Computers and musical style
David Cope · 1991
Earlier work this paper cites.
Ai methods for algorithmic composition: A survey, a critical view and future prospects
George Papadopoulos and Geraint Wiggins · 1999
Earlier work this paper cites.
Analysis and synthesis of palestrina-style counterpoint using markov chains
Mary Farbood and Bernd Schöner · 2001
Earlier work this paper cites.
Musical harmonization with constraints: A survey
François Pachet and Pierre Roy · 2001
Earlier work this paper cites.
The continuator: Musical interaction with style
Francois Pachet · 2003
Earlier work this paper cites.
Hyperscore: a graphical sketchpad for novice composers
Morwaread M Farbood, Egon Pasztor, and Kevin Jennings · 2004
Earlier work this paper cites.
Harmonising chorales by probabilistic inference
Moray Allan and Christopher KI Williams · 2005
Earlier work this paper cites.
Omax brothers: a dynamic yopology of agents for improvization learning
Gérard Assayag, Georges Bloch, Marc Chemillier, Arshia Cont, and Shlomo Dubnov · 2006
Earlier work this paper cites.
A hybrid system for automatic generation of style-specific accompaniment
Ching-Hua Chuan and Elaine Chew · 2007
Earlier work this paper cites.
Parallel successions of perfect fifths in the bach chorales
George Fitsioris and Darrell Conklin · 2008
Earlier work this paper cites.
Mysong: automatic accompaniment generation for vocal melodies
Ian Simon, Dan Morris, and Sumit Basu · 2008
Earlier work this paper cites.
music21: A toolkit for computer-aided musicology and symbolic music data
Michael Scott Cuthbert and Christopher Ariza · 2010
Earlier work this paper cites.
Real-time human interaction with supervised learning algorithms for music composition and performance
Rebecca Anne Fiebrink · 2011
Cited alongside, same era.
Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription
Nicolas Boulanger-Lewandowski, Yoshua Bengio, and Pascal Vincent · 2012
Cited alongside, same era.
Ai methods in algorithmic composition: A comprehensive survey
Jose D Fernández and Francisco Vico · 2013
Cited alongside, same era.
Composing fifth species counterpoint music with a variable neighborhood search algorithm
Dorien Herremans and Kenneth Sörensen · 2013
Cited alongside, same era.
Polyphonic music generation by modeling temporal dependencies using a RNN-DBN
Kratarth Goel, Raunaq Vohra, and JK Sahoo · 2014
Cited alongside, same era.
A deep and tractable density estimator
Deep learning techniques for music generation-a survey
Jean-Pierre Briot, Gaëtan Hadjeres, and François Pachet · 2017
Later among the works it cites.
Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
Later among the works it cites.
Deepbach: a steerable model for bach chorales generation
Gaëtan Hadjeres, François Pachet, and Frank Nielsen · 2017
Later among the works it cites.
A functional taxonomy of music generation systems
Dorien Herremans, Ching-Hua Chuan, and Elaine Chew · 2017
Later among the works it cites.
Counterpoint by convolution
Cheng-Zhi Anna Huang, Tim Cooijmnas, Adam Roberts, Aaron Courville, and Douglas Eck · 2017
Later among the works it cites.
Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Benigno Uria, Iain Murray, and Hugo Larochelle · 2014
Cited alongside, same era.
On the equivalence between deep nade and generative stochastic networks
Li Yao, Sherjil Ozair, Kyunghyun Cho, and Yoshua Bengio · 2014
Cited alongside, same era.
Style imitation and chord invention in polyphonic music with exponential families
Gaëtan Hadjeres, Jason Sakellariou, and François Pachet · 2016
Cited alongside, same era.
Bachbot: Automatic composition in the style of bach chorales
Feynman Liang · 2016
Cited alongside, same era.
Assisted lead sheet composition using flowcomposer
Alexandre Papadopoulos, Pierre Roy, and François Pachet · 2016
Cited alongside, same era.
An introduction to musical metacreation
Philippe Pasquier, Arne Eigenfeldt, Oliver Bown, and Shlomo Dubnov · 2016
Cited alongside, same era.
Neural autoregressive distribution estimation
Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, and Hugo Larochelle · 2016
Cited alongside, same era.
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E Turner, and Douglas Eck · 2017
Later among the works it cites.
Mixed-initiative generation of multi-channel sequential structures
Cheng-Zhi Anna Huang, Sherol Chen, Mark Nelson, and Doug Eck · 2018
Later among the works it cites.
Magenta. js: A javascript api for augmenting creativity with deep learning
Adam Roberts, Curtis Hawthorne, and Ian Simon · 2018
Later among the works it cites.
https://www.google.com/doodles/celebrating-johann-sebastian-bach
Celebrating Johann Sebastian Bach · 2019
Closest in time.
Consecutive 5ths and octaves in bach chorales
Luke Dahn · 2019
Closest in time.
Tensorflow. js: Machine learning for the web and beyond
Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan, Nick Kreeger, Ping Yu, Kangyi Zhang, Shanqing Cai, Eric Nielsen, David Soergel, et al · 2019
Closest in time.
Machine learning research that matters for music creation: A case study
Bob L Sturm, Oded Ben-Tal, Una Monaghan, Nick Collins, Dorien Herremans, Elaine Chew, Gaëtan Hadjeres, Emmanuel Deruty, and François Pachet · 2019
Closest in time.