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OpenMU: Your Swiss Army Knife for Music Understanding
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We present OpenMU-Bench, a large-scale benchmark suite for addressing the data scarcity issue in training multimodal language models to understand music. To construct OpenMU-Bench, we leveraged existing datasets and bootstrapped new annotations. OpenMU-Bench also broadens the scope of music understanding by including lyrics understanding and music tool usage. Using OpenMU-Bench, we trained our music understanding model, OpenMU, with extensive ablations, demonstrating that OpenMU outperforms baseline models such as MU-Llama. Both OpenMU and OpenMU-Bench are open-sourced to facilitate future research in music understanding and to enhance creative music production efficiency.
Forward citations
Cited by 2 Pith papers
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FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion
The paper contributes a 1.2M-caption and 6M-QA multimodal audio dataset generated by an LLM that fuses speech, music, sound, and visual cues, and reports downstream gains on retrieval and understanding.
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Can Large Language Models Predict Audio Effects Parameters from Natural Language?
LLMs can predict equalizer and reverb parameters from natural language descriptions, and adding DSP features, DSP function code, and few-shot examples improves the predictions.
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