| Paper ID | AUD-10.6 |
| Paper Title |
KARAOKE KEY RECOMMENDATION VIA PERSONALIZED COMPETENCE-BASED RATING PREDICTION |
| Authors |
Yuan Wang, Santa Clara University, United States; Shigeki Tanaka, NTT DOCOMO, INC., Japan; Keita Yokoyama, Hsin-Tai Wu, DOCOMO Innovations, Inc., United States; Yi Fang, Santa Clara University, United States |
| Session | AUD-10: Music Information Retrieval and Music Language Processing 2: Singing Voice |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-MIR] Music Information Retrieval and Music Language Processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
