Automatic Song Lyric Generation and Classification with Long Short-Term Networks
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. We found that LSTM networks with dropout layers were effective at lyric classification. We also found that Word embedding LSTM networks were extremely effective at lyric generation.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2019-05
Agent
- Author (aut): Tallapragada, Amit
- Thesis director: Ben Amor, Heni
- Committee member: Caviedes, Jorge
- Contributor (ctb): Computer Science and Engineering Program
- Contributor (ctb): Computer Science and Engineering Program
- Contributor (ctb): Barrett, The Honors College