Freesound Audio Tagging 2019 11st place

This is the code for freesound audio tagging 2019 on Kaggle, also DCASE 2019 task2. More details about the task can be found at:
http://dcase.community/challenge2019/task-audio-tagging,
https://www.kaggle.com/c/freesound-audio-tagging-2019/overview .

Requirement:
pytorch >= 1.0.0
librosa
numpy
matplotlib
pickle
random
pandas
sklearn

Final submission:

On curated dataset, 5 fold cv, inception v3, single model LB is 0.691 and 5 model average is 0.713;
On curated dataset and noisy dataset, with specific loss function, 5 fold cv, 8 layer cnn, 5 model average 0.678;
Geometric average of these two models, 0.731.

Code introduction:

0ExplorerDataAnalysis.ipynb
-- explore the given dataset, to check file names, labels,
-- calculate the summary information,
-- listen to samples with specific label.

0data_preparation.ipynb
-- extract the features from sound file, normalization,
-- save as one file for train and test.

2dcnn-curated-single.ipynb
-- single model train on curated subset,
-- with data augment methods, mixup, SpecAugment,
-- with disigned loss fucntion, weighted_BCE,
-- with differenct learning rate setup, CosineAnnealing, ReduceLR, CyclicLR,
-- with genernal cnn structures in image field.

2dcnn-curated-cv-tta1.ipynb
-- 5 fold cross validation of the above single model train on curated subset.

2dcnn-all-cv.ipynb
-- 5 fold cross validation on all the data, both noisy and curated.
-- 4 different loss functions are tested.

More details can be found in the technical paper:
http://dcase.community/documents/challenge2019/technical_reports/DCASE2019_Liu_38_t2.pdf