Music Sequence Prediction with Mixture Hidden Markov Models

@article{Li2019MusicSP,
  title={Music Sequence Prediction with Mixture Hidden Markov Models},
  author={Tao Li and M. Choi and Kaiming Fu and L. Lin},
  journal={2019 IEEE International Conference on Big Data (Big Data)},
  year={2019},
  pages={6128-6132}
}
  • Tao Li, M. Choi, +1 author L. Lin
  • Published 2019
  • Computer Science
  • 2019 IEEE International Conference on Big Data (Big Data)
  • Recommendation systems that automatically generate personalized music playlists for users have attracted tremendous attention in recent years. Nowadays, most music recommendation systems rely on item-based or user-based collaborative filtering or content-based approaches. In this paper, we propose a novel mixture hidden Markov model (HMM) for music play sequence prediction. We compare the mixture model with state-of-the-art methods and evaluate the predictions quantitatively and qualitatively… CONTINUE READING
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