Musictm-dataset For Joint Representation Learning Among Sheet Music, Lyrics, And Musical Audio

Abstract

This work present a music dataset named MusicTM-Dataset, which is utilized in improving the representation learning ability of different types of cross-modal retrieval (CMR). Little large music dataset including three modalities is available for learning representations for CMR. To collect a music dataset, we expand the original musical notation to synthesize audio and generated sheet-music image, and build musical notation based sheet-music image, audio clip and syllable-denotation text as fine-grained alignment, such that the MusicTM-Dataset can be exploited to receive shared representation for multimodal data points. The MusicTM-Dataset presents 3 kinds of modalities, which consists of the image of sheet-music, the text of lyrics and synthesized audio, their representations are extracted by some advanced models. In this paper, we introduce the background of music dataset and express the process of our data collection. Based on our dataset, we achieve some basic methods for CMR tasks

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keyzeng2020musictm

Related papers

• Towards Robust And Truly Large-scale Audio-sheet Music Retrieval (2023)4.52
• Exploring Modality-agnostic Representations For Music Classification (2021)0.00
• Wikimute: A Web-sourced Dataset Of Semantic Descriptions For Music Audio (2023)5.24
• Multimodal Metric Learning For Tag-based Music Retrieval (2020)9.76
• Music4all A+A: A Multimodal Dataset For Music Information Retrieval Tasks (2025)0.95
• Towards End-to-end Audio-sheet-music Retrieval (2016)0.00
• Cross-modal Music Retrieval And Applications: An Overview Of Key Methodologies (2019)12.68
• Enriching Music Descriptions With A Finetuned-llm And Metadata For Text-to-music Retrieval (2024)7.50