Genre-conditioned Acoustic Models for Automatic Lyrics Transcription of Polyphonic Music
| Autor: | Xiaoxue Gao, Chitralekha Gupta, Haizhou Li |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing |
| DOI: | 10.48550/arxiv.2204.03307 |
| Popis: | 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. Comment: 5 pages, 1 figure, accepted by IEEE ICASSP 2022 |
| Databáze: | OpenAIRE |
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