f-rumblefish

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# parameters for architecture
input_shape = (224, 224, 3)
num_classes = 6
conv_size = 32

# parameters for training
batch_size = 32

f-rumblefish

# import library ---------------------------------------------------------------
import pygame.midi
import time

# define all the constant values -----------------------------------------------
device = 0     # device number in win10 laptop
instrument = 9 # http://www.ccarh.org/courses/253/handout/gminstruments/
note_Do = 48   # http://www.electronics.dit.ie/staff/tscarff/Music_technology/midi/midi_note_numbers_for_octaves.htm
note_Re = 50
note_Me = 52

f-rumblefish

file_name   one_hot_encoding                ordered_labels  ordered_categories
----------------------------------------------------------------------------------------------------
0.png       [0, 1, 1, 1, 0, 0, 1, 0, 0, 0]  [6, 3, 2, 1]    [Shirt, Dress, Pullover, Trouser]
1.png       [1, 0, 0, 0, 1, 1, 1, 0, 0, 0]  [5, 4, 0, 6]    [Sandal, Coat, T-shirt/top, Shirt]
2.png       [1, 1, 0, 1, 0, 0, 0, 0, 0, 0]  [1, 3, 0, 3]    [Trouser, Dress, T-shirt/top, Dress]
3.png       [0, 0, 0, 0, 0, 1, 1, 1, 0, 0]  [5, 5, 7, 6]    [Sandal, Sandal, Sneaker, Shirt]

f-rumblefish

# import library from Scikit-Learn ---------------------------------------------
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix

# algorithm 1 ------------------------------------------------------------------
print(" Naive Bayes ... ")

start = timeit.default_timer()
from sklearn import naive_bayes
classifier = naive_bayes.GaussianNB()

f-rumblefish

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model

input_img = Input(shape=(28, 28, 1)) 

x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)

f-rumblefish

f-rumblefish

f-rumblefish

# algorithm 1 ------------------------------------------------------------------
print(" Naive Bayes ... ")

from sklearn import naive_bayes

classifier = naive_bayes.GaussianNB()
nb_model = classifier.fit(X, Y)
prediction = nb_model.predict(X_test)

print(" accuracy = ", accuracy_score(Y_test, prediction)) 

f-rumblefish

# import library
from imblearn.over_sampling import SMOTE

# define the environment variable
seed = 100
k = 1

# apply SMOTE to create the new dataset
sm = SMOTE(sampling_strategy='auto', k_neighbors=k, random_state=seed)
X_res, y_res = sm.fit_resample(pd_mfcc, pd_label)

f-rumblefish

# import library
import nlpaug
import nlpaug.augmenter.audio as naa

# loudness augmenter (where file_data is the output of librosa.load)
aug = naa.LoudnessAug(factor=(2, 5))
augmented_data = aug.augment(file_data)

# MFCC feature extraction for the new data ...