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arXiv · 1902.06797

End-to-end Lyrics Alignment for Polyphonic Music Using an Audio-to-Character Recognition Model

Daniel Stoller·Simon Durand·Sebastian Ewert
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Abstract

Time-aligned lyrics can enrich the music listening experience by enabling karaoke, text-based song retrieval and intra-song navigation, and other applications. Compared to text-to-speech alignment, lyrics alignment remains highly challenging, despite many attempts to combine numerous sub-modules including vocal separation and detection in an effort to break down the problem. Furthermore, training required fine-grained annotations to be available in some form. Here, we present a novel system based on a modified Wave-U-Net architecture, which predicts character probabilities directly from raw audio using learnt multi-scale representations of the various signal components. There are no sub-modules whose interdependencies need to be optimized. Our training procedure is designed to work with weak, line-level annotations available in the real world. With a mean alignment error of 0.35s on a standard dataset our system outperforms the state-of-the-art by an order of magnitude.

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BibTeXRIS

Daniel Stoller, Simon Durand, Sebastian Ewert. 2019-02-18. End-to-end Lyrics Alignment for Polyphonic Music Using an Audio-to-Character Recognition Model. https://arxiv.org/abs/1902.06797

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