Chris Donahue Unity Global Graduate Fellowship Recipient UC San Diego

1. GenerativeArt–MadewithUnity
Low- and high-level
learning problems
in game audio
Chris Donahue
Unity Global Graduate Fellowship Recipient
UC San Diego
cdonahue@ucsd.edu
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2. Spectrum of audio tasks in games
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Low level→ →High level

3. Spectrum of audio tasks in games
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Low level→ →High level

4. Low-level: generation of raw audio
sound effects
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5. Sound effects in video games are iconic
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“This NPC looks familiar”
Reused visual assets can jeopardize immersion Reused sound assets can do the same; two characters fire the
“AWP” from CSGO nearly simultaneously

7. Can we generate audio assets
procedurally?
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Generative adversarial networks
Generative adversarial networks
(Goodfellow et al. 2014) learn to generate
multimedia simply by observing examples
Different identities and poses imagined by a
generative adversarial network

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Adversarial audio synthesis
Google “WaveGAN” for
more information

10. Mid-level: generation of expressive
chiptune music
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Chiptunes are making a comeback!
RP2A03

12. Problem
Can we model four-voice NES
chiptunes and sample from that
model to generate new music?
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Dataset preparation
Visit nesmdb.ML to learn more

14. Modeling NES chiptunes at low- and high-level representations
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Low-level machine code High-level piano roll

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Thank you.
Chris Donahue
Unity Global Graduate Fellowship Recipient
UC San Diego
cdonahue@ucsd.edu