MusicSem: A Semantically Rich Language--Audio Dataset of Natural Music Descriptions

2026-02-19

AI Summary

Existing music language–audio datasets struggle to capture the rich semantic intent expressed in natural language by users, limiting multimodal music models’ ability to understand human-like descriptions. To address this gap, this work introduces and publicly releases MusicSem, a novel dataset comprising 32,493 natural language descriptions systematically extracted from authentic music discussions on Reddit. The study proposes a five-dimensional semantic taxonomy encompassing descriptiveness, atmosphere, situational context, metadata, and discourse context. Through rigorous data mining, semantic annotation, and multimodal alignment evaluation, this research substantially expands the breadth and depth of expressive language in music-related tasks, exposes critical limitations of current models in fine-grained semantic understanding, and establishes a high-quality benchmark for human-aligned music retrieval and generation.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Music representation learning is central to music information retrieval and generation. While recent advances in multimodal learning have improved alignment between text and audio for tasks such as cross-modal music retrieval, text-to-music generation, and music-to-text generation, existing models often struggle to capture users'expressed intent in natural language descriptions of music. This observation suggests that the datasets used to train and evaluate these models do not fully reflect the broader and more natural forms of human discourse through which music is described. In this paper, we introduce MusicSem, a dataset of 32,493 language-audio pairs derived from organic music-related discussions on the social media platform Reddit. Compared to existing datasets, MusicSem captures a broader spectrum of musical semantics, reflecting how listeners naturally describe music in nuanced and human-centered ways. To structure these expressions, we propose a taxonomy of five semantic categories: descriptive, atmospheric, situational, metadata-related, and contextual. In addition to the construction, analysis, and release of MusicSem, we use the dataset to evaluate a wide range of multimodal models for retrieval and generation, highlighting the importance of modeling fine-grained semantics. Overall, MusicSem serves as a novel semantics-aware resource to support future research on human-aligned multimodal music representation learning.

Problem

• music representation learning
• natural language descriptions
• multimodal learning
• semantic alignment
• music semantics

Innovation

• music semantics
• multimodal dataset
• natural language descriptions
• music representation learning
• human-aligned AI