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**MERIT** maps audio to three *disentangled* 128-dimensional unit vectors — one each for **melody**, **rhythm**, and **timbre** similarity. A single frozen [MERT-v1-330M](https://huggingface.co/m-a-p/MERT-v1-330M) backbone feeds three small trained projection heads that each specialize in one musical factor.
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> Code & training pipeline → [github.com/AMAAI-Lab/MERIT](https://github.com/AMAAI-Lab/MERIT)
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**MERIT** maps audio to three *disentangled* 128-dimensional unit vectors — one each for **melody**, **rhythm**, and **timbre** similarity. A single frozen [MERT-v1-330M](https://huggingface.co/m-a-p/MERT-v1-330M) backbone feeds three small trained projection heads that each specialize in one musical factor.
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> Code & training pipeline → [github.com/AMAAI-Lab/MERIT](https://github.com/AMAAI-Lab/MERIT)
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- [Paper](https://arxiv.org/abs/2605.27346)
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- [Paper TLDR;](https://arxivexplained.com/papers/merit-learning-disentangled-music-representations-for-audio-similarity)
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