Music Recognition Using Blockchain Technology and Deep Learning

Algorithm 1

Key Code of Music Sequence Generation.

	Def generate_notes (model, network_input, note_name, notes_len):
	Randindex = np.random.randint (0, len (network_input) − 1)
	Notedic = dict ((i, j) for i, j in enumerate (note_name)) # restore values to tones
	Pattern = network_input [randindex]
	Prediction = []
	# Randomly generate 1000 notes
	For note_index in range (1000):
	Prediction_input = np.reshape (pattern, (1, len(pattern), 1))
	Prediction_input = prediction_input/float (notes_len) # normalize
	Prediction = model.predict (prediction_input, verbose = 0)
	Index = np.argmax (prediction)
	Result = notedic [index]
	Prediction.append (result)
	Pattern.append (index)
	Pattern = pattern [1 : len(pattern)]
	Return prediction
	# Generate mid music
	Def create_music():
	Network_input, normal_network_input, notes_len, note_name = train()
	# Find the weight file with the smallest loss as the training parameter
	Files = os.listdir()
	Minloss = {}
	For i in files:
	If ‘weights’ in i:
	Num = i[11 : 15]
	Minloss [num] = i
	Best_weights = minloss [min(minloss.keys())]
	Model = get_model (normal_network_input, notes_len, best_weights)
	Prediction = generate_notes (model, network_input, note_name, notes_len)
	Offset = 0
	Output_notes = []
	# Generate note or chord objects
	For data in prediction:
	If (‘.’ in data) or data.isdigit():
	Notes_in_chord = data.split (’,')
	Notes = []
	For current_note in notes_in_chord:
	New_note = note.Note (int (current_note))
	New_note.storedInstrument = instrument.Piano()
	Notes.append (new_note)
	New_chord = chord.Chord (notes)
	New_chord.offset = offset
	Output_notes.append (new_chord)
	Else:
	New_note = note.Note (data)
	New_note.offset = offset
	New_note.storedInstrument = instrument.Piano()
	Output_notes.append (new_note)
	Offset+ = 1
	# Create a music stream
	Midi_stream = stream.Stream (output_notes)
	# Write MIDI file
	Midi_stream.write (‘midi’, fp = ‘output1.mid’)