Melody-Lyrics Matching with Contrastive Alignment Loss
Changhong Wang, Michel Olvera, Ga\"el Richard

TL;DR
This paper introduces a new melody-lyrics matching task using a contrastive alignment loss, enabling retrieval of lyrics for melodies without needing alignment annotations, leveraging self-supervised learning and a novel syllable-level lyric representation.
Contribution
It proposes a self-supervised contrastive learning framework for melody-lyrics matching that does not require alignment annotations and introduces a new syllable-level lyric representation.
Findings
The method effectively matches melodies with coherent lyrics.
It leverages existing paired melody-lyrics data without annotations.
Empirical results demonstrate the approach's potential.
Abstract
The connection between music and lyrics is far beyond semantic bonds. Conceptual pairs in the two modalities such as rhythm and rhyme, note duration and syllabic stress, and structure correspondence, raise a compelling yet seldom-explored direction in the field of music information retrieval. In this paper, we present melody-lyrics matching (MLM), a new task which retrieves potential lyrics for a given symbolic melody from text sources. Rather than generating lyrics from scratch, MLM essentially exploits the relationships between melody and lyrics. We propose a self-supervised representation learning framework with contrastive alignment loss for melody and lyrics. This has the potential to leverage the abundance of existing songs with paired melody and lyrics. No alignment annotations are required. Additionally, we introduce sylphone, a novel representation for lyrics at syllable-level…
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Taxonomy
TopicsMusic and Audio Processing · Music Technology and Sound Studies
