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Contemporary Machine Learning for Audio and Music Generation on the Web: Current Challenges and Potential Solutions

Grierson, Mick; Yee-King, Matthew; McCallum, Louis; Kiefer, Chris and Zbyszynski, Michael. 2019. 'Contemporary Machine Learning for Audio and Music Generation on the Web: Current Challenges and Potential Solutions'. In: ICMC/NYCEMF 2019. New York, United States 16-23 June 2019. [Conference or Workshop Item]

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Abstract or Description

We evaluate specific Web-based technologies that can be used to implement complex contemporary Machine Learning systems for Computer Music research, in particular for the problem of audio signal generation. As a result of greater investment from large corporations including Google and Facebook in areas such as the development of Web-based, accelerated, cross-platform Machine Learning libraries, alongside greater interest and engagement from the academic community in exploring such approaches, Machine Learning is becoming much more prevalent on the Web. This could have great potential impact for Computer Music research, acting to democratise access to complex, accelerated Machine Learning technologies through increased usability and flexibility, in tandem with clear documentation and examples. However, some problems remain in relation to the creation of more complete Machine Learning pipe-lines for Music and Sound generation. We discuss some key potential challenges in this area, and attempt to evaluate some relevant solutions for developing more accessible Computer Music Machine Learning systems.

Item Type:

Conference or Workshop Item (Paper)

Additional Information:

This work is funded by the Arts and Humanities Re-search Council as part of the MIMIC project, UK grant reference AH/R002657/1.

Keywords:

Machine Learning, Machine Learning systems, Computer Music, audio signal generation, Web-based technologies

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
23 June 2019Published

Event Location:

New York, United States

Date range:

16-23 June 2019

Item ID:

26616

Date Deposited:

15 Jul 2019 15:18

Last Modified:

17 Jul 2019 05:38

URI:

http://research.gold.ac.uk/id/eprint/26616

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