How to generate AI music
Artificial intelligence
Machines that use artificial intelligence can carry out tasks by simulating the human mind. Some of the most typical tasks performed by artificial intelligence are listed below:
Speech recognition
Fraud detection
Natural language processing
Apart from the tasks listed above, AI is now being incorporated into almost all areas of life, whether minor or major.
AI music
AI music, as the term suggests, is music generated using artificial intelligence. AI-generated music has gained immense popularity due to the advancements in machine learning algorithms and its various applications, e.g., video games, musical soundtracks, and advertising.
AI music generation
Several methods can be used to generate AI music. One of the most popular and simplest methods is to use an AI music generator. Or we can also use machine learning models to generate music. Some of the most popular AI music generators are the following:
Jukebox
AIVA
Beatoven.ai
Ecrett music
Apart from using AI music generators, we can also use the following methodologies for creating AI-generated music:
Rule-based systems: We can specify rules and constraints by using rule-based systems. These rules and constraints then define the type that will be generated and how it will be generated. For example, we can specify that the melody should use a specific pattern, i.e., a beat or a rhythm.
Machine learning: We can also use machine learning to generate music. We can train the model using single or multiple datasets of existing music using this approach. This enables the model to learn the patterns and structures of the music and then generate new music that follows the pattern of the music the dataset is trained on. Some of the machine learning models are:
MelodyRNN
MusicVAE
MuseNet
Hybrid approach: This approach combines elements of rule-based systems and machine learning to generate music. Using this approach, we can have the best of two worlds, i.e., we can use the strengths of both of the methodologies mentioned above to generate creative and structured music.
Example
Run the widget to generate the music using the music21 toolkit.
import music21
# create a stream object to hold the music data
melody_data = music21.stream.Stream()
# add a key signature of C major to the melody_data
melody_data.append(music21.key.Key('C'))
# define a list of pitches for the melody
pitches_collection = ['C4', 'D4', 'E4', 'F4', 'G4', 'A4', 'B4', 'C5']
# define a list of durations for the melody
durations_data = [music21.duration.Duration(1.0)] * len(pitches_collection)
# create a list of notes by pairing the pitches and durations
list_notes = [music21.note.Note(pitch, duration=dur) for pitch, dur in zip(pitches_collection, durations_data)]
# add the notes to the melody stream
for note in list_notes:
melody_data.append(note)
# save the music to a MIDI file
melody_data.write('midi', fp='generated_music.mid')
print("The music has been successfully generated")Explanation
Line 1: We import the
music21library.Line 4–7: We create a stream object to hold the music data and then add a key signature of
Cmajor to the melody.Line 10: We define a list of pitches that'll be used to create the melody.
Line 13: We define a list of durations for the melody. Each duration is set to
1.0quarter note (i.e., one beat).Line 16: We create a list of note objects by pairing the pitches and durations using the
zip()function. Each note object is created with a pitch and a duration.Line 19–20: We add each note to the melody stream using a
forloop.Line 23–24: We write the melody to the
generated_music.midfile and then print a message to the screen indicating that the music has been successfully generated.
The quality of the music generated depends on the type of data the model is trained on, the model itself, and also the techniques used to generate the music using AI.
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