2022/04/07 by Xiaoxue Gao, Gao, Xiaoxue, Chitralekha Gupta +3
Arts and Humanities · Computer Science · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #Diverse Musicological Studies #FOS: Computer and information sciences #FOS: Electrical engineering #Music Technology and Sound Studies #Music and Audio Processing #Sound (cs.SD) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2204.03307
openalex publication_date 2022/04/07 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Lyrics transcription of polyphonic music is challenging not only because the singing vocals are corrupted by the background music, but also because the background music and the singing style vary across music genres, such as pop, metal, and hip hop, which affects lyrics intelligibility of the song in different ways. In this work, we propose to transcribe the lyrics of polyphonic music using a novel genre-conditioned network. The proposed network adopts pre-trained model parameters, and incorporates the genre adapters between layers to capture different genre peculiarities for lyrics-genre pairs, thereby only requiring lightweight genre-specific parameters for training. Our experiments show that the proposed genre-conditioned network outperforms the existing lyrics transcription systems.