Aligned Music Notation and Lyrics Transcription

• URL: http://arxiv.org/abs/2412.04217v1
• Date: Thu, 05 Dec 2024 14:50:11 GMT
• Title: Aligned Music Notation and Lyrics Transcription
• Authors: Eliseo Fuentes-Martínez, Antonio Ríos-Vila, Juan C. Martinez-Sevilla, David Rizo, Jorge Calvo-Zaragoza,
• Abstract summary: This paper introduces and formalizes the Aligned Music Notation and Lyrics Transcription (AMNLT) challenge. It addresses the complete transcription of vocal scores by jointly considering music symbols, lyrics, and their synchronization. We evaluate different approaches to address this challenge, ranging from traditional divide-and-conquer methods that handle music and lyrics separately, to novel end-to-end solutions.
• Score: 8.411893399638046
• License:
• Abstract: The digitization of vocal music scores presents unique challenges that go beyond traditional Optical Music Recognition (OMR) and Optical Character Recognition (OCR), as it necessitates preserving the critical alignment between music notation and lyrics. This alignment is essential for proper interpretation and processing in practical applications. This paper introduces and formalizes, for the first time, the Aligned Music Notation and Lyrics Transcription (AMNLT) challenge, which addresses the complete transcription of vocal scores by jointly considering music symbols, lyrics, and their synchronization. We analyze different approaches to address this challenge, ranging from traditional divide-and-conquer methods that handle music and lyrics separately, to novel end-to-end solutions including direct transcription, unfolding mechanisms, and language modeling. To evaluate these methods, we introduce four datasets of Gregorian chants, comprising both real and synthetic sources, along with custom metrics specifically designed to assess both transcription and alignment accuracy. Our experimental results demonstrate that end-to-end approaches generally outperform heuristic methods in the alignment challenge, with language models showing particular promise in scenarios where sufficient training data is available. This work establishes the first comprehensive framework for AMNLT, providing both theoretical foundations and practical solutions for preserving and digitizing vocal music heritage.

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