
Description
Lyric classification and generation are trending in topics in the machine learning community. Long Short-Term Networks (LSTMs) are effective tools for classifying and generating text. We explored their effectiveness in the generation and classification of lyrical data and proposed methods of evaluating their accuracy.
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Contributors
- Tallapragada, Amit (Author)
- Ben Amor, Heni (Thesis director)
- Caviedes, Jorge (Committee member)
- Computer Science and Engineering Program (Contributor, Contributor)
- Barrett, The Honors College (Contributor)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2019-05
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