Genre-conditioned Acoustic Models for Automatic Lyrics Transcription of Polyphonic Music

TL;DR

This paper introduces a genre-conditioned neural network for automatic lyrics transcription in polyphonic music, effectively handling genre-specific variations and outperforming existing systems.

Contribution

It proposes a novel genre-conditioned model with lightweight genre adapters, improving lyrics transcription accuracy across diverse music genres.

Findings

Outperforms existing lyrics transcription systems

Uses pre-trained models with genre-specific adapters

Effective across multiple music genres

Abstract

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.