# CLEANED DATA VISUAL ANALYSIS (signal, fft, fbank, mfcc) SECTION
print("\n\n--------------------Started cleaned data visual analysis for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_male, name=confv.dataset_shemo_male))
da.visual_analysis(df=data_info_shemo_df_m, database=confv.database_shemo, status=confv.clean, gender=confv.gender_male, envelope=False, resample=False)
# This is same as,
# da.visual_analysis(df=data_info_shemo_df_m, database=confv.database_shemo, status=confv.original, gender=confv.gender_male, envelope=True, resample=True)
# Since these cleaned data are already equipped with envelope and resampling, setting them to False or True does not matter.
# (envelope and resample does not matter when its clean)
print("--------------------Finished cleaned data visual analysis for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_male, name=confv.dataset_shemo_male))
'''
# Building Features
print("\n\n--------------------Started building features for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_male, name=confv.dataset_shemo_male))
classes = list(np.unique(data_info_shemo_df_m.stress_emotion))
mconf_shemo_m = confc.ModelConfig(database=confv.database_shemo, gender=confv.gender_male, mode=confv.ml_mode_convolutional, classes=classes)
print(mconf_shemo_m.database)
print(mconf_shemo_m.gender)
print(mconf_shemo_m.mode)
print(mconf_shemo_m.nfilt)
print(mconf_shemo_m.nfeat)
print(mconf_shemo_m.nfft)
print(mconf_shemo_m.step)
print(mconf_shemo_m.classes)
print(mconf_shemo_m.features_save_name)
print(mconf_shemo_m.model_config_save_name)
print(mconf_shemo_m.training_log_name)
print(mconf_shemo_m.model_save_name)
print(mconf_shemo_m.model_h5_save_name)
print(mconf_shemo_m.model_tflite_save_name)
print(mconf_shemo_m.feature_path)
print("\n\n--------------------Started cleaned data visual analysis for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_female, name=confv.dataset_cremad_female))
da.visual_analysis(df=data_info_cremad_df_f, database=confv.database_cremad, status=confv.clean, gender=confv.gender_female, envelope=False, resample=False)
# This is same as,
# da.visual_analysis(df=data_info_cremad_df_f, database=confv.database_cremad, status=confv.original, gender=confv.gender_female, envelope=True, resample=True)
# Since these cleaned data are already equipped with envelope and resampling, setting them to False or True does not matter.
# (envelope and resample does not matter when its clean)
print("--------------------Finished cleaned data visual analysis for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_female, name=confv.dataset_cremad_female))
'''
# Building Features
print(
"\n\n--------------------Started building features for adjusted and {gender} isolated dataset: {name}--------------------"
.format(gender=confv.gender_female, name=confv.dataset_cremad_female))
classes = list(np.unique(data_info_cremad_df_f.stress_emotion))
mconf_cremad_f = confc.ModelConfig(database=confv.database_cremad,
gender=confv.gender_female,
mode=confv.ml_mode_convolutional,
classes=classes)
print(mconf_cremad_f.database)
print(mconf_cremad_f.gender)
print(mconf_cremad_f.mode)
print(mconf_cremad_f.nfilt)
print(mconf_cremad_f.nfeat)
print(mconf_cremad_f.nfft)
print(mconf_cremad_f.step)
print(mconf_cremad_f.classes)
print(mconf_cremad_f.features_save_name)
print(mconf_cremad_f.model_config_save_name)
print(mconf_cremad_f.training_log_name)
print(mconf_cremad_f.model_save_name)
print(mconf_cremad_f.model_h5_save_name)
print(mconf_cremad_f.model_tflite_save_name)
print("\n\n--------------------Started cleaned data visual analysis for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_male, name=confv.dataset_ravdess_male))
da.visual_analysis(df=data_info_ravdess_df_m, database=confv.database_ravdess, status=confv.clean, gender=confv.gender_male, envelope=False, resample=False)
# This is same as,
# da.visual_analysis(df=data_info_ravdess_df_m, database=confv.database_ravdess, status=confv.original, gender=confv.gender_male, envelope=True, resample=True)
# Since these cleaned data are already equipped with envelope and resampling, setting them to False or True does not matter.
# (envelope and resample does not matter when its clean)
print("--------------------Finished cleaned data visual analysis for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_male, name=confv.dataset_ravdess_male))
'''
# Building Features
print(
"\n\n--------------------Started building features for adjusted and {gender} isolated dataset: {name}--------------------"
.format(gender=confv.gender_male, name=confv.dataset_ravdess_male))
classes = list(np.unique(data_info_ravdess_df_m.stress_emotion))
mconf_ravdess_m = confc.ModelConfig(database=confv.database_ravdess,
gender=confv.gender_male,
mode=confv.ml_mode_convolutional,
classes=classes)
print(mconf_ravdess_m.database)
print(mconf_ravdess_m.gender)
print(mconf_ravdess_m.mode)
print(mconf_ravdess_m.nfilt)
print(mconf_ravdess_m.nfeat)
print(mconf_ravdess_m.nfft)
print(mconf_ravdess_m.step)
print(mconf_ravdess_m.classes)
print(mconf_ravdess_m.features_save_name)
print(mconf_ravdess_m.model_config_save_name)
print(mconf_ravdess_m.training_log_name)
print(mconf_ravdess_m.model_save_name)
print(mconf_ravdess_m.model_h5_save_name)
print(mconf_ravdess_m.model_tflite_save_name)