Black-box membership inference on text-to-music models reaches up to 98.6% accuracy by training an auditor on semantic alignment patterns extracted from shadow-model generations.
Map-music2vec: A simple and effective baseline for self-supervised music audio representation learning,
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Extending E4SRec with multimodal content features on LastFM-1K yields up to 95% Recall and 79% NDCG gains over ID-only baselines, though naive fusion does not always improve results.
Pretrained audio models show large performance gaps between standard MIR tasks and music recommendation in both hot and cold-start settings.
citing papers explorer
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Auditing Training Data in Generative Music Models via Black-Box Membership Inference
Black-box membership inference on text-to-music models reaches up to 98.6% accuracy by training an auditor on semantic alignment patterns extracted from shadow-model generations.
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Multimodal Music Recommendation System using LLMs
Extending E4SRec with multimodal content features on LastFM-1K yields up to 95% Recall and 79% NDCG gains over ID-only baselines, though naive fusion does not always improve results.
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Adopting State-of-the-Art Pretrained Audio Representations for Music Recommender Systems
Pretrained audio models show large performance gaps between standard MIR tasks and music recommendation in both hot and cold-start settings.