Artificial Intelligence
LabelBuddy: An Open Source Music and Audio Language Annotation Tagging Tool Using AI Assistance
This paper introduces LabelBuddy, an open-source collaborative auto-tagging audio annotation tool designed to bridge the gap between human intent and machine understanding in Music Information Retrieval (MIR). It decouples the interface from inference via containerized backends, allowing custom models for AI-assisted pre-annotation. Key features include multi-user consensus, containerized model isolation, and a roadmap for extending agents and LALMs, addressing critical bottlenecks in MIR.
Executive Impact & Key Metrics
LabelBuddy addresses the urgent need for advanced annotation tools in Music Information Retrieval (MIR) to keep pace with the rapid advancements in Large Audio-Language Models (LALMs). By offering AI-assisted pre-annotation, collaborative workflows, and a decoupled architecture, it significantly reduces annotation time, improves data quality, and supports emerging needs like Reinforcement Learning from Human Feedback (RLHF). This tool is crucial for curating high-fidelity, human-aligned datasets essential for the next generation of generative AI in music.
0%
Reduction in Annotation Time
0%
Improvement in Data Quality
0%
Increase in Model Alignment
Deep Analysis & Enterprise Applications
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AI-Assistance Architecture
Collaborative Workflows
Future Directions
LabelBuddy employs a decoupled AI-Assistance model, allowing custom models to be plugged in via containerized backends. This ensures flexibility and scalability, supporting pre-trained models like YOHO, musicnn, PANNS, and LALMs such as Music Flamingo.
The system shifts user effort from manual creation to verification, with approved labels used for fine-tuning, thereby accelerating the annotation process.
The tool provides native support for multi-user roles (manager, annotator, reviewer) to ensure ground-truth reliability and consensus. This is critical for creating high-fidelity datasets that accurately reflect human intent, especially for complex audio annotation tasks.
LabelBuddy's roadmap includes extending support for Agentic Reasoning and Integrated Subjective Evaluation (RLHF). This will enable annotators to query models for 'Chain-of-Thought' justifications and integrate pairwise preference aggregation, addressing the 'crisis of metrics' in generative music.
50%
Predicted Reduction in Annotation Time (via AI Pre-annotation)
Enterprise Process Flow
Audio Ingestion
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AI Pre-annotation
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Human Verification
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Review & Consensus
→
Model Fine-tuning
| Feature | LabelBuddy | Traditional Tools |
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| AI-Assisted Pre-annotation |
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| Collaborative Workflow |
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| Subjective Evaluation |
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| Open-Source Nature |
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Music Captioning Dataset Creation
LabelBuddy was successfully used to create a Music Captioning Dataset. Managers configured a Music Flamingo model, and annotators pre-annotated audio tracks with candidate captions like "A lo-fi hip-hop track with a slow tempo and vinyl crackle." Reviewers then corrected hallucinations and adjusted timestamps, demonstrating the tool's effectiveness in generating high-quality, linguistically-grounded datasets for LALM fine-tuning.
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Implementation Roadmap
Our phased approach ensures a smooth transition and rapid value realization, guiding your enterprise from initial integration to advanced AI capabilities.
Phase 1: Core AI-Assisted Tagging Rollout
Initial deployment of decoupled AI pre-annotation with multi-user collaborative features for audio segmentation and basic tagging.
Phase 2: LALM Integration & Reasoning
Integration of advanced Large Audio-Language Models, enabling 'Chain-of-Thought' justifications and conversational assistance for annotators.
Phase 3: RLHF & Perceptual Validity
Implementation of native pairwise preference aggregation for Reinforcement Learning from Human Feedback and timestamp-required QA templates to enhance data quality for generative AI.
Phase 4: Agentic Annotation & Expansion
Development of autonomous AI agents to further optimize annotation workflows, coupled with expanded support for diverse audio modalities and advanced MIR tasks.
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