Introduction

Generative AI is artificial intelligence that can produce new data, similar to textbooks, images, or music. In music composition, generative AI empowers creators to generate new warbles, chimes, measures, and even entire songs. This technology can potentially revolutionize how music is created, with some artists and musicians already utilizing it to produce new and innovative works. There are two main approaches to using generative AI in music composition.

One approach is to train an AI algorithm on a large music dataset. The algorithm learns the patterns and structures of music, utilizing this knowledge to generate new music that closely resembles the training data. Another approach is using AI to produce new musical ideas not grounded in music. Do this by using AI to induce arbitrary sequences of notes or by using AI to explore the space of possible musical combinations.

Learning Objectives

• Learn about generative AI and how it is changing how music is composed.
• Discover the many advantages of generative AI, from musical inspiration to customized production.
• Examine the difficulties and ethical issues involved in incorporating AI-generated music into the realm of the arts.
• Learn about the current uses of generative AI in music creation and its potential in the future.

This article was published as a part of the Data Science Blogathon.

Understanding Generative AI

• Artificial intelligence radically changes music composition by using modern machine learning algorithms to create original music compositions independently. By studying large datasets and documenting the critical lessons within the music. These models can create melodies, rhythms, and harmonies that display artistic expression and consistency. This helps the composers to study new possibilities and vanquish creativity by giving fresh ideas in the musical field.
• Applying this GenAI model in music composition usually involves advanced machine learning algorithms like RNNs, Variational Autoencoders (VAEs), or Transformers. All these algorithms act as the foundation of this model. Let the model perceive and create the music based on the data the model has learned on. The music composers and developers will have a hold on the ML substructures like PyTorch and TensorFlow to construct and teach. Testing with various network architectures, training techniques, and hyperparameters to maximize the standard and innovation of the created music.
• The training of AI models for the composition of music includes revealing the model to the vast range of music genres, styles, etc. Our model will learn statistical patterns, melodic motifs, progressions of chords, and rhythmic elements from the data used as input. Create its composition by selecting the required data from the learned patterns. This will result in getting outputs that are unique and original and that can fascinate the audience.

Benefits of Generative AI in Music Composition

The generative AI model provides the benefits that increase and motivate the music compositions using advanced ML algorithms and a large dataset of musical notes.

Following are some of the benefits of this model:

Inspiration and Novelty

This AI model is a source of new ideas for music composers, giving them vast and new ideas for creating music. By understanding the various music types and styles, the Generative AI model can create unique variations and combinations, which can threaten music composers in the future. The creative process is energized by this injection of novelty and inspiration, which leads to the development of novel concepts and musical horizons. Composers can learn new music regions and do trials on playful kinds of music, harmonies, and tunes they had not thought of before.

The capability of this model to create new ideas for composing fresh music removes the big hurdle of creativity, which will help the music composers. This inspiration and novelty not only increases the creativity among the composers but also provides the composers with the opportunity to explore their creative boundaries and help in the enhancement of the music industry or world.

Efficiency and Time Savings

Using this model has changed the perspective of the composition of music by benefiting us with time-saving ability. By using advanced machine learning algorithms and a vast range of music datasets, This model can quickly generate many musical notes, tunes, and variations within a matter of time. With the help of this, there is no need for music composers to start from the beginning, which helps speed up the starting of new musical creations.

Composers can take the music generated by the AI model and use or modify that according to their needs rather than spending more time on creating the initial music or thinking about how to start the music or tune. Using the musical notes quickly, the composers can go through several arrangements, styles, and melodies and proficiently experiment with them. Finally, this AI model upholds the creativity and time of composers to dig deeper into their thoughts and bring their great ideas into the world.

Exploration of Musical Styles and Genres

Because of regenerative AI, musicians have new tools to experiment with various musical genres and styles. Generative AI models help writers think outside the box and explore fresh ideas by studying melodic figures of speech from different times. This encourages them to break free from their usual creative patterns and try new things. Thanks to their adaptability, musicians can mix different influences into their music, making it diverse and unique. This leads to exploring new aesthetics and blending various musical styles.

Collaborative Possibilities

Music composers can collaborate with these AI models as innovative partners in music composition. As a result, there will be the possibility of collaboration in music composition with this generative AI model. With the help of the computational power of AI generative models, music composers will have the potential to co-produce music by merging man-made innovations. GenAI models can become good partners, providing music composers with regular ideas of new music variations and motivating their creativity process.

Overcoming Creative Blocks

Generative AI is an invaluable source of new musical ideas and variations in music for musicians. This creative process helps musicians to take new artistic paths and implement new life and originality into their music. The produced material can help composers overcome creative blocks and develop new ideas. In conclusion, Generative AI sparks creativity and encourages musicians to explore new directions in their music by providing endless possibilities.

Personalization and Customization

Generative AI makes it possible to personalize and customize music in many new ways in just seconds. AI models can create music tailored to listeners’ tastes by analyzing their history and preferences. This personalized approach makes the music more meaningful and enjoyable for the audience, increasing their engagement and satisfaction. Generative AI allows users to create music tailored to each audience’s specific taste, making it more personal and engaging. This can lead to a deeper sense of connection and satisfaction for the audience. This personalized approach makes the music more personal and engaging for the audience, enhancing their appreciation and enjoyment.

Ethical Considerations and Challenges

• Copyright and Proprietorship: In music composition, Generative AI models can upraise multiple questions regarding copyright and proprietorship.
• Imaginative Realness: This AI-generated model up-lifts the analysis concerning if the individuality and Imaginative capacity of human music composers are preserved.
• Disclosure and openness: It is necessary and important to be open and transparent about the generated music which is by AI so that the users and listeners can differentiate between the music generated by AI and whether it is original or genuine.

• Data Bias in Training: These generative AI models can sometimes exhibit discrimination towards certain data they were trained on, highlighting the need to train them on vast and comprehensive datasets to prevent such biasing.
• Adjusting Human and simulated intelligence Innovativeness: As we are using these generative AI models to their fullest in the world of music, it will be essential to maintain human creativity and the computational power of AI so that human creativity will not become extinct.
• Effects on Human Musicians: As the potential of these generative AI models is increasing daily, there is a rise of concern about human creativity and innovation and the future of humans in this world of AI tools.

Applications of Generative AI in Music

• Media Production of Music: Generative AI enables the generation of music specifically tailored to the needs of media production This music can set the mood, enhance the story, and engage the audience.
• Intuitive Music Encounters: Generative AI allows for interactive music experiences where the music responds and adapts to user input or real-time data, creating immersive and personalized musical journeys. Use this application in interactive installations, augmented reality experiences, and live performances.
• Remixing and Inspecting: Generative AI models can now analyze existing music and create remixes of them. These AI models can also inspect the existing music and recommend changes in specific song parts. This is possible because of the AI-powered analysis technique.
• Music Creation and Sound Plan: Generative AI can help with music production and sound design by automating audio mixing, mastering, and generating sound effects. It can create high-quality, unique sounds that enhance the production value and complement the composition.
• Help with the Writing: Composers can utilize generative AI models as a tool to brainstorm new lyrics and integrate them into their original music. AI assists in generating a wide range of musical concepts, including melodies, harmonies, and rhythms, offering valuable starting points for composition. This can help composers spark their creativity and develop new and innovative ideas.

Techniques to Implement

Repetitive Brain Organizations (RNNs)

RNNs are good at capturing sequential patterns and can create songs or rhythms by predicting the following note based on the previous notes.

Path to dataset: https://www.kaggle.com/datasets/imsparsh/musicnet-dataset

import numpy as np
import pretty_midi
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Dense

# Loading the MIDI file and preprocess the data
def load_midi_file(file_path):
    midi_data = pretty_midi.PrettyMIDI(file_path)

    # Ensure all data bytes are within the valid range (0 to 127)
    for instrument in midi_data.instruments:
        for note in instrument.notes:
            note.velocity = np.clip(note.velocity, 0, 127)

    return midi_data

# Loading the MusicNet dataset
def load_dataset(path_to_dataset):
    piano_rolls = []
    for file_path in path_to_dataset:
        midi_data = load_midi_file(file_path)
        piano_roll = midi_data.get_piano_roll(fs=25)  # Sampling at 25 Hz
        piano_rolls.append(piano_roll)

    return np.array(piano_rolls)

# Creating sequences of piano rolls
def create_sequences(dataset, sequence_length):
    sequences = []
    for piano_roll in dataset:
        for i in range(0, piano_roll.shape[1] - sequence_length):
            sequence = piano_roll[:, i:i+sequence_length]
            sequences.append(sequence)
    return np.array(sequences)

# Loading the MusicNet dataset (Replace 'path_to_dataset' 
# with the actual path to your MIDI files)
dataset = load_dataset(path_to_dataset=['/Users/Admin/Downloads/2186_vs6_1.mid', 
'/Users/Admin/Downloads/2191_vs6_5.mid', '/Users/Admin/Downloads/2194_prelude13.mid'])

# Hyperparameters
sequence_length = 100  # Length of input sequences
input_shape = dataset.shape[1:]  
output_shape = dataset.shape[1:]  
num_units = 256  # Number of units in the LSTM layer
dropout_rate = 0.3  # Dropout rate for regularization

# Creating sequences
sequences = create_sequences(dataset, sequence_length)

X = sequences[:, :-1]
y = sequences[:, -1]

# Creating and compileing the model
model = Sequential()
model.add(LSTM(num_units, input_shape=input_shape, return_sequences=True))
model.add(Dropout(dropout_rate))
model.add(LSTM(num_units))
model.add(Dropout(dropout_rate))
model.add(Dense(np.prod(output_shape), activation='sigmoid'))
model.add(Reshape(output_shape))
model.compile(loss='binary_crossentropy', optimizer='adam')

# Training the model
model.fit(X, y, epochs=50, batch_size=128)

# Generating music using the trained model
def generate_music(model, seed_sequence, length):
    generated_sequence = np.array(seed_sequence)

    for _ in range(length):
        next_step = model.predict(np.expand_dims(generated_sequence[-sequence_length:], axis=0))
        generated_sequence = np.vstack((generated_sequence, next_step[0]))

    return generated_sequence

seed_sequence = np.random.randint(0, 2, size=(input_shape[0], sequence_length))
generated_music = generate_music(model, seed_sequence, length=200)

generated_midi = pretty_midi.PrettyMIDI()
instrument = pretty_midi.Instrument(program=0)  # Use the first instrument (Acoustic Grand Piano)
for pitch in range(output_shape[0]):
    note_starts = np.where(generated_music[pitch] > 0.5)[0]
    note_ends = np.where(generated_music[pitch] <= 0.5)[0]
    if len(note_starts) > len(note_ends):
        note_ends = np.append(note_ends, output_shape[1] - 1)
    for start, end in zip(note_starts, note_ends):
        note = pretty_midi.Note(velocity=64, pitch=pitch, start=start/25, end=(end+1)/25)
        instrument.notes.append(note)
generated_midi.instruments.append(instrument)
generated_midi.write('/Users/Admin/Downloads/generated_music.mid')

Autoencoders with Variation (VAEs)

VAEs are generative models that can learn the underlying latent space of musical information. VAEs can then sample from this latent space to create new musical compositions with desired characteristics.

import numpy as np
import pretty_midi
from keras.models import Model
from keras.layers import Input, LSTM, Dropout, Dense, Lambda
from keras.losses import binary_crossentropy
from keras import backend as K

# Loading the MIDI file and preprocess the data (same as before)
def load_midi_file(file_path):
    #Same as before

# Loading the MusicNet dataset (same as before)
def load_dataset(path_to_dataset):
    #Same as before

# Creating sequences of piano rolls (same as before)
def create_sequences(dataset, sequence_length):
    #Same as before

# Loading the MusicNet dataset (Replace 'path_to_dataset' 
# with the actual path to your MIDI files)
dataset = load_dataset(path_to_dataset=['/Users/Admin/Downloads/2186_vs6_1.mid', 
'/Users/Admin/Downloads/2191_vs6_5.mid', '/Users/Admin/Downloads/2194_prelude13.mid'])

# Hyperparameters
sequence_length = 100  # Length of input sequences
input_shape = dataset.shape[1:]  
output_shape = dataset.shape[1:]  
num_units = 256  # Number of units in the LSTM layer
dropout_rate = 0.3  # Dropout rate for regularization

# Creating sequences (same as before)
sequences = create_sequences(dataset, sequence_length)

X = sequences[:, :-1]
y = sequences[:, -1]

# Creating the VAE model
def sampling(args):
    z_mean, z_log_var = args
    epsilon = K.random_normal(shape=(K.shape(z_mean)[0], K.int_shape(z_mean)[1]))
    return z_mean + K.exp(0.5 * z_log_var) * epsilon

inputs = Input(shape=input_shape)
x = LSTM(num_units, return_sequences=True)(inputs)
x = Dropout(dropout_rate)(x)
x = LSTM(num_units)(x)
x = Dropout(dropout_rate)(x)

# Latent space
z_mean = Dense(64)(x)
z_log_var = Dense(64)(x)

# Sampling
z = Lambda(sampling)([z_mean, z_log_var])

# Decoder layers
decoder_input = Input(shape=(64,))
x = Dense(num_units)(decoder_input)
x = Dropout(dropout_rate)(x)
x = RepeatVector(sequence_length)(x)
x = LSTM(num_units, return_sequences=True)(x)
x = Dropout(dropout_rate)(x)
x = LSTM(num_units, return_sequences=True)(x)
x = Dropout(dropout_rate)(x)
outputs = Dense(np.prod(output_shape), activation='sigmoid')(x)

# VAE model
encoder = Model(inputs, z_mean)
decoder = Model(decoder_input, outputs)

outputs = decoder(encoder(inputs))
vae = Model(inputs, outputs)

# VAE loss function
def vae_loss(x, x_decoded_mean):
    reconstruction_loss = binary_crossentropy(x, x_decoded_mean) * np.prod(output_shape)
    kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
    return reconstruction_loss + kl_loss

vae.compile(optimizer='adam', loss=vae_loss)

# Training the VAE model
vae.fit(X, X, epochs=50, batch_size=128)

# Generating music using the trained VAE model
def generate_music_vae(model, seed_sequence):
    generated_sequence = model.predict(seed_sequence)
    return generated_sequence

seed_sequence = np.random.randint(0, 2, size=(1, input_shape[0], sequence_length))
generated_music = generate_music_vae(vae, seed_sequence)

# The rest of the code for creating MIDI and saving the generated music remains the same
...

Learning through reinforcement: Using reinforcement learning, Generative AI models can be trained to produce high-quality and desirable music compositions. The models learn to improve their output based on feedback and reward signals.

Style Move: Style transfer techniques allow AI models to generate music in a specific style or imitate the qualities of a specific artist or genre. The models can produce music that matches the desired style by learning style features from existing compositions.

Future Perspectives

• Tools for Making Music With AI Incorporation: AI will become a necessary part of music creation tools and software, seamlessly integrated into digital audio workstations (DAWs) and composition software. Musicians will readily have AI-powered assistance and creative tools, enabling them to explore new musical realms and streamline their workflow.
• The development of AI models: As AI research progresses, we can expect more advanced AI models specifically designed for music synthesis. These models will better capture complex musical structures, create compositions with more variety, and respond to user input in real time.
• Multimodal and cross-domain creativity: Generative AI can analyze paste music arrangements to explore cross-modal and multimodal imagination. AI models can create music that complements visual art, interactive installations, augmented reality experiences, and multisensory experiences.
• Customized Music Encounters: Generative AI will play a significant role in providing listeners with personalized music experiences. AI algorithms will analyze user preferences, listening habits, and contextual information to create personalized playlists that align with individual preferences and moods.

Conclusion

Music synthesis has been transformed by generative AI, which gives composers a huge variety of melodic ideas and genres to experiment with, fostering creativity and productivity. While AI offers intriguing possibilities, maintaining the distinctiveness of composers requires addressing ethical issues like artistic legitimacy and copyright. To maintain artistic integrity, a compromise must be struck between artificial intelligence and human creativity. Future enhancements to musical expression, personalized experiences, and collaboration between human composers and AI frameworks have a significant potential to change the music production landscape as AI models progress.

Key Takeaways

• GenAI models have provided music directors and composers with a wide range of ideas regarding new inventions and genres and also inspired them to make or generate more music.
• With the help of this generative AI model, we can take our innovative level to much higher levels. It helps explore more different ways to generate music and helps improve the process of improving.
• Make sure that the composed music is genuine and needs to deal with copyright issues.
• GenAI model helps generate different music and productivity, it cannot add a human touch or emotions.

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