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Models/Attentive-MultiResUNet

Attentive-MultiResUNet

Reported on 10 benchmarks across 2 tasks

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Music5 results

  • Music Source SeparationonMUSDB18
    SDR (avg)
    6.81
    best: 9.2 (Sparse HT Demucs (fine tuned))
  • Music Source SeparationonMUSDB18
    SDR (bass)
    5.88
    best: 10.47 (Sparse HT Demucs (fine tuned))
  • Music Source SeparationonMUSDB18
    SDR (drums)
    7.63
    best: 10.83 (Sparse HT Demucs (fine tuned))
  • Music Source SeparationonMUSDB18
    SDR (other)
    5.14
    best: 7.08 (Band-Split RNN (semi-sup.))
  • Music Source SeparationonMUSDB18
    SDR (vocals)
    8.57
    best: 10.47 (Band-Split RNN (semi-sup.))

Methodology5 results

  • 2D ClassificationonMUSDB18
    SDR (avg)
    6.81
    best: 9.2 (Sparse HT Demucs (fine tuned))
  • 2D ClassificationonMUSDB18
    SDR (bass)
    5.88
    best: 10.47 (Sparse HT Demucs (fine tuned))
  • 2D ClassificationonMUSDB18
    SDR (drums)
    7.63
    best: 10.83 (Sparse HT Demucs (fine tuned))
  • 2D ClassificationonMUSDB18
    SDR (other)
    5.14
    best: 7.08 (Band-Split RNN (semi-sup.))
  • 2D ClassificationonMUSDB18
    SDR (vocals)
    8.57
    best: 10.47 (Band-Split RNN (semi-sup.))