MERIT trains disentangled heads for melody, rhythm, and timbre via conditional audio generation and stem separation, with evaluations showing each head responds strongly to its target dimension and near chance on others across synthetic and real audio.
arXiv preprint arXiv:2103.09410 , year=
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PHALAR achieves up to 70% relative accuracy gain in stem retrieval over prior art using under half the parameters and 7x faster training by enforcing musical equivariances via spectral pooling and complex heads.
ACARec attends over artist catalogs to generate CF embeddings for new tracks, more than doubling recall and NDCG versus content-only baselines in music recommendation.
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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MERIT: Learning Disentangled Music Representations for Audio Similarity
MERIT trains disentangled heads for melody, rhythm, and timbre via conditional audio generation and stem separation, with evaluations showing each head responds strongly to its target dimension and near chance on others across synthetic and real audio.
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PHALAR: Phasors for Learned Musical Audio Representations
PHALAR achieves up to 70% relative accuracy gain in stem retrieval over prior art using under half the parameters and 7x faster training by enforcing musical equivariances via spectral pooling and complex heads.
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Leveraging Artist Catalogs for Cold-Start Music Recommendation
ACARec attends over artist catalogs to generate CF embeddings for new tracks, more than doubling recall and NDCG versus content-only baselines in music recommendation.
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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.