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Models/Meta-TasNet

Meta-TasNet

Reported on 10 benchmarks across 2 tasks · 1 paper

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)· 2020-02-17
5.52
best: 9.2 (Sparse HT Demucs (fine tuned))
Meta-learning Extractors for Music Source Separation arXiv:2002.07016
Music Source SeparationonMUSDB18
SDR (bass)· 2020-02-17
5.58
best: 10.47 (Sparse HT Demucs (fine tuned))
Meta-learning Extractors for Music Source Separation arXiv:2002.07016
Music Source SeparationonMUSDB18
SDR (drums)· 2020-02-17
5.91
best: 10.83 (Sparse HT Demucs (fine tuned))
Meta-learning Extractors for Music Source Separation arXiv:2002.07016
Music Source SeparationonMUSDB18
SDR (other)· 2020-02-17
4.19
best: 7.08 (Band-Split RNN (semi-sup.))
Meta-learning Extractors for Music Source Separation arXiv:2002.07016
Music Source SeparationonMUSDB18
SDR (vocals)· 2020-02-17
6.4
best: 10.47 (Band-Split RNN (semi-sup.))
Meta-learning Extractors for Music Source Separation arXiv:2002.07016

Methodology5 results

2D ClassificationonMUSDB18
SDR (avg)· 2020-02-17
5.52
best: 9.2 (Sparse HT Demucs (fine tuned))
Meta-learning Extractors for Music Source Separation arXiv:2002.07016
2D ClassificationonMUSDB18
SDR (bass)· 2020-02-17
5.58
best: 10.47 (Sparse HT Demucs (fine tuned))
Meta-learning Extractors for Music Source Separation arXiv:2002.07016
2D ClassificationonMUSDB18
SDR (drums)· 2020-02-17
5.91
best: 10.83 (Sparse HT Demucs (fine tuned))
Meta-learning Extractors for Music Source Separation arXiv:2002.07016
2D ClassificationonMUSDB18
SDR (other)· 2020-02-17
4.19
best: 7.08 (Band-Split RNN (semi-sup.))
Meta-learning Extractors for Music Source Separation arXiv:2002.07016
2D ClassificationonMUSDB18
SDR (vocals)· 2020-02-17
6.4
best: 10.47 (Band-Split RNN (semi-sup.))
Meta-learning Extractors for Music Source Separation arXiv:2002.07016