LabelBuddy: An Open Source Music and Audio Language Annotation Tagging Tool Using AI Assistance
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914368689537024 |
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| author | Prokopiou, Ioannis Sina, Ioannis Kounelis, Agisilaos Vikatos, Pantelis Stafylakis, Themos |
| author_facet | Prokopiou, Ioannis Sina, Ioannis Kounelis, Agisilaos Vikatos, Pantelis Stafylakis, Themos |
| contents | The advancement of Machine learning (ML), Large Audio Language Models (LALMs), and autonomous AI agents in Music Information Retrieval (MIR) necessitates a shift from static tagging to rich, human-aligned representation learning. However, the scarcity of open-source infrastructure capable of capturing the subjective nuances of audio annotation remains a critical bottleneck. This paper introduces \textbf{LabelBuddy}, an open-source collaborative auto-tagging audio annotation tool designed to bridge the gap between human intent and machine understanding. Unlike static tools, it decouples the interface from inference via containerized backends, allowing users to plug in custom models for AI-assisted pre-annotation. We describe the system architecture, which supports multi-user consensus, containerized model isolation, and a roadmap for extending agents and LALMs. Code available at https://github.com/GiannisProkopiou/gsoc2022-Label-buddy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04293 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LabelBuddy: An Open Source Music and Audio Language Annotation Tagging Tool Using AI Assistance Prokopiou, Ioannis Sina, Ioannis Kounelis, Agisilaos Vikatos, Pantelis Stafylakis, Themos Sound Artificial Intelligence Information Retrieval Machine Learning The advancement of Machine learning (ML), Large Audio Language Models (LALMs), and autonomous AI agents in Music Information Retrieval (MIR) necessitates a shift from static tagging to rich, human-aligned representation learning. However, the scarcity of open-source infrastructure capable of capturing the subjective nuances of audio annotation remains a critical bottleneck. This paper introduces \textbf{LabelBuddy}, an open-source collaborative auto-tagging audio annotation tool designed to bridge the gap between human intent and machine understanding. Unlike static tools, it decouples the interface from inference via containerized backends, allowing users to plug in custom models for AI-assisted pre-annotation. We describe the system architecture, which supports multi-user consensus, containerized model isolation, and a roadmap for extending agents and LALMs. Code available at https://github.com/GiannisProkopiou/gsoc2022-Label-buddy. |
| title | LabelBuddy: An Open Source Music and Audio Language Annotation Tagging Tool Using AI Assistance |
| topic | Sound Artificial Intelligence Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2603.04293 |