A Functional Taxonomy of Music Generation Systems
By Dorien Herremans, Ching-Hua Chuan, Elaine Chew. 2017
178 citations recorded by the source corpus.
Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.
Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Sander Dieleman
- Aäron van den Oord
- Dacheng Tao
- Andrew Zisserman
- Ming-Hsuan Yang
- Trevor Darrell
- Jitendra Malik
In Information Systems
In Signal Processing
- Sander Dieleman
- Karen Simonyan
- Aäron van den Oord
- Monica Dinculescu
- Cheng-Zhi Anna Huang
- Ed Newton-Rex
- Carrie J. Cai
- Hendrik Vincent Koops
Citations in the corpus
- The challenge of realistic music generation: modelling raw audio at scale (2018, background): “There has been significant interest in computational music generation for many decades [11, 20].”
- Human-AI Co-Creation in Songwriting (2020, methodology): “Within the music domain, there has been a long history of using AI techniques to model music composition [23, 31, 53, 54], by assisting in composing counter-point [22, 32], harmonizing melodies [13, 43, 51], more general infilling [29, 35, 41, 61], exploring more adven-turous chord progressions [27,…”
- MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer (2018, background): “For a more comprehensive overview we refer the interested readers to these surveys: [4,13,16].”