Music Genre Identificaion

This is project no.2

In this project the task is to identify the type of music genre for a particular audio file using DL.

The genres are as follow:

• Blues:0
• Classicl:1
• Country:2
• Disco:3
• Hiphop:4
• Jazz:5
• Metal:6
• Pop:7
• Reggae:8
• Rock:9

To use the audio files first we have converted them to a format that can be fed to a deep learning algorith using Audio Spectograms as such:

def create_spectogram(filename, name):
 plt.interactive(False)
 clip, sample_rate = librosa.load(filename, sr=None)
 fig = plt.figure(figsize=[0.72,0.72])
 ax = fig.add_subplot(111)
 ax.axes.get_xaxis().set_visible(False)
 ax.axes.get_yaxis().set_visible(False)
 ax.set_frame_on(False)
 S = librosa.feature.melspectrogram(y=clip, sr=sample_rate)
 librosa.display.specshow(librosa.power_to_db(S, ref=np.max))
 filename = '/content/full_data/' + name + '.jpg'
 plt.savefig(filename, dpi=400, bbox_inches='tight', pad_inches=0)
 plt.close()
 fig.clf()
 plt.close(fig)
 plt.close('all')
 del filename, name,clip, sample_rate, fig, ax, S

And utilizing a sequential network as such:

model = Sequential()

model.add(Conv2D(32, (3,3), padding='same',
                input_shape=(64,64,3)))

model.add(Activation('relu'))

model.add(Conv2D(64, (3,3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Dropout(0.25)) # initial: 0.25

model.add(Conv2D(64, (3,3), padding='same'))

model.add(Activation('relu'))

model.add(Conv2D(64, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.35))  # initial: 0.5

model.add(Conv2D(128, (3, 3), padding='same'))

model.add(Activation('relu'))

model.add(Conv2D(128, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.5))  # initial: 0.5

model.add(Flatten())

model.add(Dense(512))

model.add(Activation('relu'))

model.add(Dropout(0.5))  # initial: 0.5

model.add(Dense(10, activation='softmax'))

model.compile(optimizers.RMSprop(learning_rate=0.0001, weight_decay=1e-6), # learning_rate was 0.0005
             loss="categorical_crossentropy",
             metrics=["accuracy"])

model.summary()