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Models/EAT-S

EAT-S

Reported on 7 benchmarks across 3 tasks · 1 paper · 1 SOTA

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· 2022-04-25
    95.25
    best: 99.1 (OmniVec2)
    End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification NetworkarXiv:2204.11479
  • Audio ClassificationonESC-50
    Top-1 Accuracy· uses extra data· 2022-04-25
    95.25
    best: 99.1 (OmniVec2)
    End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification NetworkarXiv:2204.11479
  • Audio ClassificationonAudioSet
    Test mAP· 2022-04-25
    0.405
    best: 0.558 (OmniVec2)
    End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification NetworkarXiv:2204.11479

Methodology3 results

  • ClassificationonESC-50
    Accuracy (5-fold)· uses extra data· 2022-04-25
    95.25
    best: 99.1 (OmniVec2)
    End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification NetworkarXiv:2204.11479
  • ClassificationonESC-50
    Top-1 Accuracy· uses extra data· 2022-04-25
    95.25
    best: 99.1 (OmniVec2)
    End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification NetworkarXiv:2204.11479
  • ClassificationonAudioSet
    Test mAP· 2022-04-25
    0.405
    best: 0.558 (OmniVec2)
    End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification NetworkarXiv:2204.11479

Speech1 result

  • Keyword SpottingonGoogle Speech Commands
    Google Speech Commands V2 35· 2022-04-25
    98.15
    best: 99.1 (WaveFormer)
    SOTA
    End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification NetworkarXiv:2204.11479