Algorithmic Composition: Paradigms of Automated Music Generation
By G. Nierhaus. 2008
295 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/9477e18b9a230ddc5ec48e74fa62ef70be9be8be
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
In Computer Vision and Pattern Recognition
- Jean-Pierre Briot
- Gaëtan Hadjeres
- François Pachet
- Chris Donahue
- Jose D. Fernández
- Francisco J. Vico
- Yiting Li
- Huanru Henry Mao
In Signal Processing
- Jean-Pierre Briot
- Chris Donahue
- Gaëtan Hadjeres
- François Pachet
- Julian McAuley
- Jose D. Fernández
- Francisco J. Vico
- Yiting Li
Citations in the corpus
- AI Methods in Algorithmic Composition: A Comprehensive Survey (2013, background): “There have been other attempts to systematize algorithmic composition, such as the taxonomies of Papadopoulos and Wiggins (1999) and Nierhaus (2009).”
- Deep Learning Techniques for Music Generation - A Survey (2017, background): “• Roads’ book about computer music [146]; • Cope’s [24], Dean and McLean’s [30] and/or Nierhaus’ books [132] about algorithmic composition; • a recent survey about AI methods in algorithmic composition [44]; and • Cope’s book about models of musical creativity [25].”
- LakhNES: Improving Multi-instrumental Music Generation with Cross-domain Pre-training (2019, background): “Most early work involved manually encoding musical rules into generative systems or rearranging fragments of human-composed music; see [8] for an extensive overview.”