Musicological Interpretability in Generative Transformers
Author:
Affiliation:
1. Yale University,Dept of Music,New Haven,CT,USA
2. WordWood Collective,Louiville,KY,USA
3. Harvard University,Department of Music,Cambridge,MA
4. University of Massachusetts Amherst,Department of Music and Dance,Amherst,MA,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10335168/10335097/10335202.pdf?arnumber=10335202
Reference70 articles.
1. From artificial neural networks to deep learning for music generation: history, concepts and trends
2. Music generation with deep learning;Kalingeri,2016
3. Deep learning for music;Huang,2016
4. Audio Chord Recognition with Recurrent Neural Networks;Boulanger-Lewandowski;ISMIR. Curitiba,2013
5. Functional Harmony Recognition of Symbolic Music Data with Multi-task Recurrent Neural Networks;Chen;ISMIR.,2018
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