kapre example for blog.py

from keras.models import Sequential
from kapre.time_frequency import Melspectrogram
from kapre.utils import Normalization2D
from kapre.augmentation import AdditiveNoise

# 6 channels (!), maybe 1-sec audio signal
input_shape = (6, 44100) 
sr = 44100
model = Sequential()
# A mel-spectrogram layer with
# no decibel conversion for some reasons and (return_decibel=False)
# amplitude, not power (power=1.0)
model.add(Melspectrogram(n_dft=512, n_hop=256, input_shape=src_shape,
                         border_mode='same', sr=sr, n_mels=128,
                         fmin=0.0, fmax=sr/2, power=1.0,
                         return_decibel=False, trainable_fb=False,
                         trainable_kernel=False
                         name='trainable_stft'))
# Maybe some additive white noise.
model.add(AdditiveNoise(power=0.2))
# If you wanna normalise it per-frequency
model.add(Normalization2D(str_axis='freq')) # or 'channel', 'time', 'batch', 'data_sample'
# After this, it's just a usual keras workflow. For example..
# Add some layers, e.g., model.add(some convolution layers..)
# Compile the model
model.compile('adam', 'categorical_crossentropy') # if single-label classification
# train it with raw audio sample inputs
x = load_x() # e.g., x.shape = (10000, 6, 44100)
y = load_y() # e.g., y.shape = (10000, 10) if it's 10-class classification
# and train it
model.fit(x, y)
# write a paper and graduate or get paid. Profit!