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My solution on DCASE task1, using feed forward attention based model and ensemble and got 83.6% accuracy on test set.

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data/TUT-acoustic-scenes-2016-development

data/TUT-acoustic-scenes-2016-development

 
 

models_yaml

models_yaml

 
 

src

src

 
 

useful_models

useful_models

 
 

.gitignore

.gitignore

 
 

EULA.pdf

EULA.pdf

 
 

README.html

README.html

 
 

README.md

README.md

 
 

batch.py

batch.py

 
 

class_attention.py

class_attention.py

 
 

cnn.py

cnn.py

 
 

dnn.py

dnn.py

 
 

do_system_evaluation.py

do_system_evaluation.py

 
 

ensemble.py

ensemble.py

 
 

ensemble.yaml

ensemble.yaml

 
 

ff_attention.py

ff_attention.py

 
 

ff_avg.py

ff_avg.py

 
 

gether_result.sh

gether_result.sh

 
 

layers.py

layers.py

 
 

lstm.py

lstm.py

 
 

lstm_attention.py

lstm_attention.py

 
 

lstm_average.py

lstm_average.py

 
 

print_labels.py

print_labels.py

 
 

requirements.txt

requirements.txt

 
 

residual_network.py

residual_network.py

 
 

task1_scene_classification.py

task1_scene_classification.py

 
 

task1_scene_classification.yaml

task1_scene_classification.yaml

 
 

task1_scene_classification_cnn.yaml

task1_scene_classification_cnn.yaml

 
 

task1_scene_classification_dnn.yaml

task1_scene_classification_dnn.yaml

 
 

task1_scene_classification_ff_att_class.yaml

task1_scene_classification_ff_att_class.yaml

 
 

task1_scene_classification_lstm.yaml

task1_scene_classification_lstm.yaml

 
 

task1_scene_classification_lstm_att.yaml

task1_scene_classification_lstm_att.yaml

 
 

task1_scene_classification_lstm_avg.yaml

task1_scene_classification_lstm_avg.yaml

 
 

task3_sound_event_detection_in_real_life_audio.py

task3_sound_event_detection_in_real_life_audio.py

 
 

task3_sound_event_detection_in_real_life_audio.yaml

task3_sound_event_detection_in_real_life_audio.yaml

 
 

test_model.py

test_model.py

 
 

test_multi_processing.py

test_multi_processing.py

 
 

train2train_and_val.py

train2train_and_val.py

 
 

utils.py

utils.py

 
 

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My solution on DCASE task1, using feed forward attention based model and ensemble and got 83.6% accuracy on test set.

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