2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-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
SessionAUD-10: Music Information Retrieval and Music Language Processing 2: Singing Voice
LocationGather.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
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.