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

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.

Related

May 2021 | CHI

Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits

Brianna Richardson, Jean Garcia-Gathright, Samuel F. Way, Jennifer Thom, Henriette Cramer

April 2021 | The Web Conference

Where To Next? A Dynamic Model of User Preferences

Francesco Sanna Passino, Lucas Maystre, Dmitrii Moor, Ashton Anderson, Mounia Lalmas

April 2021 | AISTATS

Collaborative Classification from Noisy Labels

Lucas Maystre, Nagarjuna Kumarappan, Judith Bütepage, Mounia Lalmas