How Can an AI Model Create Music?

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While AI-generated music can demonstrate technical proficiency and adhere to established musical standards, there are concerns among certain critics regarding its ability to convey genuine human creativity and emotional depth. AI-generated music can be a supplement to human creativity rather than a replacement for it. The role of AI in music creation is still a topic of discussion, but it is expected to bring a revolution in the music industry.


In order to create any type of art, whether it’s a painting, musical composition, sculpture, or something else, one requires imagination and creativity. Until recently, only the human brain was thought to be capable of imagining and producing art. With the emergence of AI technologies, machines have proven to also be capable of creating art, a process known as generative art. By using deep learning techniques, an autonomous system like AI can produce images, melodies, and other forms of art based on the inputs or prompts.

Role of AI in music creation 

Artificial intelligence (AI) has the potential to create music in various ways. One of the most popular methods involves using machine learning algorithms, particularly deep neural networks, to analyze large datasets of existing music and then generate new compositions based on the analysis.

The process of creating music with AI involves training the ML algorithm on a dataset of existing music, which could be a vast collection of songs in a particular genre or style. The algorithm examines the patterns and structures in the music, such as the chords, melodies, beats, rhythms, and instrumentation, and then uses this information to create new music that is similar in style and structure.

Music representation to ML

When it comes to developing AI-based music, the primary obstacle is how to translate music into a comprehensible format for the machine learning model. Since the model perceives information as a numerical vector, we must depict music as a series of numeric tokens that carry information about the rhythm, notes, timbre, and other relevant data points. These tokens serve as a representation of music that can be processed by the AI system.

One way to train the model is by utilizing MIDI files, which is a widely adopted protocol in the electronic music industry for transmitting musical information between digital devices. MIDI files are structured files that include ordered information on notes, rhythm changes, BPM (beats per minute), and other related data points, which can be treated as a natural language representation for training the model.

Many machine learning algorithms rely on using the raw audio data at each time step as input. These inputs are usually represented in the form of sequential input vectors, which are commonly used in natural language processing (NLP) to train the model. Then the model can predict the next token in a sequence at each time step.


AI-generated music platforms

Artificial Intelligence (AI) is rapidly changing the world of music creation. AI-generated music platforms are using machine learning algorithms to create unique and original music for videos, games, movies, and many other media projects.

Following are some of the best AI-generated music platforms available.

  • AIVA – AIVA Technologies, a startup based in Luxembourg, has developed AIVA (Artificial Intelligence Virtual Artist), an AI-powered music composition system that employs deep learning algorithms to generate unique musical pieces.
  • Amper Music – Amper Music is a cloud-based platform for music composition that utilizes artificial intelligence to enable users to quickly and easily create and personalize their own music. By utilizing machine learning algorithms, Amper Music generates music that is tailored to the preferences of the user.
  • Google’s Magenta – Magenta is an open-source research platform developed by Google that employs machine learning algorithms to produce music and art. It offers a range of tools and models for music analysis, generation, and performance, which has made it a favored option for musicians and researchers alike.
  • OpenAI’s MuseNet – Developed by OpenAI, MuseNet is an AI-powered music generation system that can produce compositions in diverse styles and genres. This is achieved through an analysis and learning process from a vast array of musical pieces from various cultures and time periods.
  • Amadeus Code – Amadeus Code is an AI-based music composition platform that specializes in generating pop and rock music. With the help of this platform, users can create personalized music tracks based on their desired genre, tempo, and key.
  • Jukedeck – Jukedeck is a well-known platform for generating music using AI, which employs machine learning algorithms to produce custom, royalty-free music tracks. The platform has found wide use among video producers, game developers, and other creative professionals for generating unique and original music.

AI-generated music platforms are making it easier to create original and high-quality music tracks. Whether you are a musician, a music composer, or a media creator, there is an AI music platform that can help you to achieve your goal. The platforms listed above are some of the best available in the market with their unique features. You can choose as per your requirement and start creating music.

Legal concerns 

The use of artificial intelligence (AI) in music creation has raised a number of legal concerns that need to be addressed. Here are some of the key legal issues related to AI-generated music.

  • Copyright issues: One of the major legal issues related to AI-generated music is the question of copyright ownership. If the AI model creates music based on some input, it may not be clear who owns the copyright to the music. The copyright may belong to the AI system user, the AI system itself, or the creator of the AI tool.
  • Licensing issues: AI-generated music may require a license to use. It will depend upon the intended use of the music and copyright.
  • Compensation for rights holders – AI-generated music is developed based on some input. If the input itself is copyrighted music, then who is going to compensate the rights holders? The other question is how the rights holder proves that their music has been used to create new AI music.
  • Moral rights: Some laws provide creators of original music with moral rights, giving them the right to claim authorship of their original work. They can object to any changes or uses of their work that they find objectionable. It is still not clear whether these rights extend to AI-generated music as well.

As the use of AI in music creation continues to grow, it is likely that these legal issues will continue to evolve and be addressed by lawmakers and courts. It is crucial for developers and users of AI-generated music to stay informed about the latest legal developments. They should seek legal advice when necessary to ensure compliance with the new legal terms and conditions.


AI-generated music can be impressive in terms of its technical proficiency and adherence to established musical standards. However, some critics argue that AI-created music cannot have the same level of emotional depth and human creativity. Therefore, it is important to consider AI-generated music as an augmentation of human creativity but not to replace it. Ultimately, the role of AI in music creation remains a topic of debate, but it is definitely going to revolutionize the music industry in the coming days. Only the future can tell how AI technology will continue to evolve in the musical world.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…