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deepcomposer

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deepcomposer [2015/10/25 10:42]
bigpianist [Data Format]
deepcomposer [2015/10/25 17:28] (current)
eraoul [Summary]
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 | Authors | Anna Aljanaki, Stefan Balke, Ryan Groves, Eugene Krofto, Eric Nichols | | Authors | Anna Aljanaki, Stefan Balke, Ryan Groves, Eugene Krofto, Eric Nichols |
-| Affiliation | Fake University | 
 | Code | [[https://​github.com/​stefan-balke/​hamr2015-lstm-music-gen|Github Link]] | | Code | [[https://​github.com/​stefan-balke/​hamr2015-lstm-music-gen|Github Link]] |
  
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   * Collect several symbolic song datasets, with melody and possibly chords   * Collect several symbolic song datasets, with melody and possibly chords
   * Represent data in a common vector format appropriate for input to a neural net   * Represent data in a common vector format appropriate for input to a neural net
-  * Develop ​an LSTM architecture for generation of melody/​chord output.+  * Develop ​a Long Short-Term Memory (LSTMarchitecture for generation of melody/​chord output.
   * **Goal:** Given a melody and chord sequence, generate melody with chords.   * **Goal:** Given a melody and chord sequence, generate melody with chords.
   * Make music!   * Make music!
Line 120: Line 119:
   * 12 Pitch Classes (Chroma) with chord information.   * 12 Pitch Classes (Chroma) with chord information.
   * 5 levels of the metrical hierarchy.   * 5 levels of the metrical hierarchy.
 +
 ===== Libraries Used ===== ===== Libraries Used =====
  
Line 127: Line 127:
   * SQL Alchemy   * SQL Alchemy
   * NumPy   * NumPy
 +
 +===== Results =====
 +
 +==== Train on ESAC, Random Seed ====
 +
 +{{:​example_rnd_01.png?​800|}}
 +{{:​example_rnd_01.mp3|}}
 +
 +==== Train on ESAC, ESAC Seed, Probabilistic Sampling ====
 +
 +{{:​example_rnd_02.png?​800|}}
 +{{:​example_rnd_02.mp3|}}
 +===== Next Steps =====
 +
 +  * Try out longer training and more epoches.
 +  * Integrate harmony components.
 +  * Cross-learn:​ Learn on ESAC and harmony from jazz etc.
deepcomposer.1445784146.txt.gz ยท Last modified: 2015/10/25 10:42 by bigpianist