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Models/M2D/0.7

M2D/0.7

Reported on 8 benchmarks across 3 tasks · 1 paper

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

Audio3 results

Audio ClassificationonESC-50
Accuracy (5-fold)· uses extra data· 2024-04-09
96
best: 99.1 (OmniVec2)
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework arXiv:2404.06095
Audio ClassificationonESC-50
Top-1 Accuracy· uses extra data· 2024-04-09
96
best: 99.1 (OmniVec2)
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework arXiv:2404.06095
Audio ClassificationonAudioSet
Test mAP· 2024-04-09
0.479
best: 0.558 (OmniVec2)
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework arXiv:2404.06095

Methodology3 results

ClassificationonESC-50
Accuracy (5-fold)· uses extra data· 2024-04-09
96
best: 99.1 (OmniVec2)
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework arXiv:2404.06095
ClassificationonESC-50
Top-1 Accuracy· uses extra data· 2024-04-09
96
best: 99.1 (OmniVec2)
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework arXiv:2404.06095
ClassificationonAudioSet
Test mAP· 2024-04-09
0.479
best: 0.558 (OmniVec2)
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework arXiv:2404.06095

Speech2 results

Speaker IdentificationonVoxCeleb1
Accuracy· uses extra data· 2024-04-09
96.3
best: 96.6 (MSM-MAE)
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework arXiv:2404.06095
Speaker IdentificationonVoxCeleb1
Top-1 (%)· uses extra data· 2024-04-09
96.3
best: 96.6 (MSM-MAE)
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework arXiv:2404.06095