Introduces an OpenMIC-derived multi-axis benchmark sequence showing that high binary instrument QA accuracy fails to predict robust grounding, with models showing position bias, confusable errors, and temporal bias.
OpenMU: Your swiss army knife for music understanding
2 Pith papers cite this work. Polarity classification is still indexing.
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TinyMU is a 229M-parameter compact music understanding model that achieves 82% of state-of-the-art large audio-language model performance on the MuChoMusic benchmark while being 35 times smaller.
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Beyond Binary Instrument QA: Probing Instrument Grounding in Music Audio-Language Models
Introduces an OpenMIC-derived multi-axis benchmark sequence showing that high binary instrument QA accuracy fails to predict robust grounding, with models showing position bias, confusable errors, and temporal bias.
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TinyMU: A Compact Audio-Language Model for Music Understanding
TinyMU is a 229M-parameter compact music understanding model that achieves 82% of state-of-the-art large audio-language model performance on the MuChoMusic benchmark while being 35 times smaller.