2D CNN's promising via Kaggle.
1 on leaderboard: http://dcase.community/documents/challenge2018/technical_reports/DCASE2018_Jeong_102.pdf
2 on leaderboard: https://cpjku.github.io/dcase_task2/
3 on leaderboard: http://dcase.community/documents/challenge2018/technical_reports/DCASE2018_Iqbal_89.pdf
Ensemble of top methods?
https://www.kaggle.com/daisukelab/freesound-dataset-kaggle-2018-solution
ch 7: (advanced prac)
model ensembling (of 2D CNN & Combined 1D CNN & RNN combo, etc??)
Replace Conv2D w/ DepthwiseConv2D (or maybe SeperableConv2D?)
Batch normailization
Inception
Residual connections
DenseNet?
ch 6: (Sequential methods)
Staley - 1D CNN is preprocessing step before RNN
. Bidirectional RNNs, recurrent dropout, & stacking RNNS
ch 5: (Convolution)
Tune HP (# neurons, layers, epochs, batch_size)
. Dropout, regularization
. Data augmentation - No
. Use a pretrained CNN? (VGG16)
. Fine-tuning (VGG16)
K-Fold Validation
2D CNN's promising via Kaggle.
1 on leaderboard: http://dcase.community/documents/challenge2018/technical_reports/DCASE2018_Jeong_102.pdf
2 on leaderboard: https://cpjku.github.io/dcase_task2/
3 on leaderboard: http://dcase.community/documents/challenge2018/technical_reports/DCASE2018_Iqbal_89.pdf
Ensemble of top methods?
https://www.kaggle.com/daisukelab/freesound-dataset-kaggle-2018-solution
ch 7: (advanced prac)
model ensembling (of 2D CNN & Combined 1D CNN & RNN combo, etc??)
Replace Conv2D w/ DepthwiseConv2D (or maybe SeperableConv2D?)
Batch normailization
Inception
Residual connections
DenseNet?
ch 6: (Sequential methods)
Staley - 1D CNN is preprocessing step before RNN
. Bidirectional RNNs, recurrent dropout, & stacking RNNS
ch 5: (Convolution)
Tune HP (# neurons, layers, epochs, batch_size)
. Dropout, regularization
. Data augmentation - No
. Use a pretrained CNN? (VGG16)
. Fine-tuning (VGG16)
K-Fold Validation