Karaoke Key Recommendation Via Personalized Competence-Based Rating Prediction

Yuan Wang; Shigeki Tanaka; Keita Yokoyama; Hsin-Tai Wu; Yi Fang

ICASSP 2021accepted0 citations

Karaoke Key Recommendation Via Personalized Competence-Based Rating Prediction

Yuan Wang, Shigeki Tanaka, Keita Yokoyama, Hsin-Tai Wu, Yi Fang

Abstract

Karaoke machines have become a popular choice for many people’s daily entertainment. In this paper, we address a novel task of recommending a suitable key for a user to sing a given song to meet his or her vocal competence, by proposing the Personalized Competence-based Rating Prediction (PCRP) model. Specifically, we learn the song embedding vectors from the sequences of songs’ notes, and then design a history encoder with recurrent units to extract users’ vocal information from the history rating records and utilize a rating decoder based on the Transformer. The experimental results on a real world karaoke rating dataset demonstrate the effectiveness of the proposed approach.

BibTeX

@inproceedings{icassp2021_karaokekeyrecomm,
  title = {Karaoke Key Recommendation Via Personalized Competence-Based Rating Prediction},
  author = {Yuan Wang and Shigeki Tanaka and Keita Yokoyama and Hsin-Tai Wu and Yi Fang},
  booktitle = {ICASSP 2021},
  year = {2021}
}