return {'melody': melody, 'harmony': harmony}
# Generate melody and harmony based on sound features for feature in sound_features: # Simple example: generate a melody based on sound frequency melody.append(feature['frequency'] * 2) belami mick
# Load sound file def load_sound(file_path): sample_rate, data = wavfile.read(file_path) return sample_rate, data return {'melody': melody
import numpy as np from scipy.io import wavfile data = wavfile.read(file_path) return sample_rate
# Example usage file_path = 'example_sound.wav' sample_rate, data = load_sound(file_path) composition = generate_music(sample_rate, data) print(composition) This code snippet demonstrates a basic sound analysis and music generation process. The actual implementation of Belami Mick would require a more sophisticated approach, incorporating AI and machine learning techniques.
return composition
# Generate musical composition composition = music_generation_engine(sound_features)