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AI Beatles Michelle RNN for only melody line
MP3•Episode home
Manage episode 281298138 series 1122024
Content provided by Keizo Akutagawa and Start by Keizo Akutagawa. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Keizo Akutagawa and Start by Keizo Akutagawa or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
This melody line playing on the piano was composed by AI with deep learning algorithm which used standalone Keras with a Tensorflow 1.14 backend. It was trained by Beatles song "Michelle" MIDI notes divided into 12 bars and played with "Michelle" accompaniment on LogicPro X. Using the method of extracting the characteristics of the composer by calculating the probability distribution of the pitch and length of the note that comes after each note. The official code repository for examples in the O'Reilly book 'Generative Deep Learning' https://learning.oreilly.com/library/view/generative-deep-learning/9781492041931/ https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines/dp/1492041947/ref=sr_1_1
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136 episodes
MP3•Episode home
Manage episode 281298138 series 1122024
Content provided by Keizo Akutagawa and Start by Keizo Akutagawa. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Keizo Akutagawa and Start by Keizo Akutagawa or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
This melody line playing on the piano was composed by AI with deep learning algorithm which used standalone Keras with a Tensorflow 1.14 backend. It was trained by Beatles song "Michelle" MIDI notes divided into 12 bars and played with "Michelle" accompaniment on LogicPro X. Using the method of extracting the characteristics of the composer by calculating the probability distribution of the pitch and length of the note that comes after each note. The official code repository for examples in the O'Reilly book 'Generative Deep Learning' https://learning.oreilly.com/library/view/generative-deep-learning/9781492041931/ https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines/dp/1492041947/ref=sr_1_1
…
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136 episodes
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