A framework learns to map seed music embeddings to mood-adjusted targets using proxy sampling and a joint objective, outperforming baselines in preserving non-mood attributes on two datasets.
Unsuper- vised pitch-timbre-variation disentanglement of monophonic music signals based on random perturbation and re-entry train- ing,
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Controllable Embedding Transformation for Mood-Guided Music Retrieval
A framework learns to map seed music embeddings to mood-adjusted targets using proxy sampling and a joint objective, outperforming baselines in preserving non-mood attributes on two datasets.