How Does AI Music Work? From Machine Learning to Viral Hits

Whether you like it or not, AI generated music is getting good… like really good. And with 60% of young creators already using AI in their workflows, it doesn’t look like AI generated music is going away any time soon.

So how do machines make music composition easier? How do they make it better? How do they produce music at all?

In this article, we’ll take a closer look at the story of AI music. Where it’s at, how it’s used, and how you can get involved.

A Brief History of AI Generated Music

The idea of using artificial intelligence to generate music has been around for decades, with early experiments in algorithmic composition dating back to the 1950s and 60s.

Turing in front of a large tube computer circa 1950

Early AI compositions used rule based systems for basic music production. The computer was given a set of rules which it could follow to generate music.

Major breakthroughs in AI music came in the late 1990s and early 2000s as neural networks and deep learning allowed artificial intelligence systems to analyze and learn from existing music data.

Companies like Amper Music and Aiva emerged, using AI to generate royalty-free music or even collaborating with human artists to compose pop songs.

Today, AI music generators have become easily accessible for anyone who chooses to use them in their music production flow. Novel AI tools such as AI cover song generators are gaining popularity among creators on social media due to their unique and attention grabbing attributes.

How Does AI Music Work?

AI music works by training machine learning algorithms on existing musical data to generate new musical compositions and enhance existing ones.

How Does AI Music Work

AI generated music produced by tools like Boomy, Soundraw, and others are often created by AI algorithms which has followed a similar pattern. Let’s take a closer look at the life cycle of AI generated music, from model training to the final human touches.

1. Data Collection

The first step in AI music generation is collecting large datasets for the AI music algorithms to learn from. The AI music generator needs access to thousands or even millions of examples of existing music compositions and audio across different genres like pop, rock, classical, jazz etc.

Typically this data is in the form of MIDI files, musical scores, or mp3s and wav files. The music industry provides a wealth of training data for artificial intelligence systems to analyze musical patterns, instruments, melodies, harmonies, rhythms, and other features and musical styles.

With enough data covering a fair chunk of existing human musical creativity, An AI music generator can build the foundations to create new AI music in any style. The quality and range of the training data directly impact how well the AI generated music will resonate with its audience.

As is always the case with AI – the more data, the better!

2. Feature Extraction

Once an AI system has a large set of musical data, the next step to creating AI music is extracting meaningful features that represent different aspects of the compositions.

The raw music data is processed using an AI technology known as digital signal processing to identify common patterns like melodies, chords, rhythm, and instrumentation.

These musical structures are then translated into mathematical representations that machine learning algorithms can understand. For example, musical elements like pitch and rhythm can be encoded numerically as frequencies and durations.

The extracted features act as a distillation of the key elements. This allows the AI model to learn the statistical relationships between features, building an abstract understanding of how humans construct music, and what we like to hear.

3. Model Training

In model training, the machine learning algorithms are trained using the extracted musical features from the dataset.

Neural networks are commonly used, which learn by optimizing weighting parameters over many iterations. The goal is to build an AI music model that can estimate the probability of certain musical sequences occurring based on the patterns it observed in the training data.

Techniques like backpropagation and reinforcement learning update the model parameters to improve its ability to accurately generate music features matching the original data distribution.

With sufficient training iterations on broad the neural network builds an advanced understanding of musical theory and common practices, the algorithm is now ready to create it’s first piece of AI music.

4. Music Generation

So far, data has been collected, processed, and used to train a neural network. We’re now ready to use the model to create AI generated music.

The music generator creates music note-by-note, chord-by-chord in a procedural manner based on its training dataset. It samples from its probability distributions to select musical features that form coherent compositions matching the training data.

AI generated music allows for rapid creation of production-ready tunes in any style. The generated compositions often sound very good, while retaining the style and feeling of the existing music the AI analyzed during training.

If you’ve ever tried these tools, you might have been pleasantly surprised at how good the AI generated music can be.

5. Post-Processing

After AI music generators produce new compositions, post-processing is essential to refine the output for practical use in the music industry and elsewhere.

This phase often involves human intervention to enhance the AI-generated music, making it indistinguishable from existing music produced by human artists. Common tasks include mixing, mastering, and arranging, which elevate the quality of the AI-generated compositions to professional standards.

Post-processing serves as the final step to polish the AI generated music, ensuring that the output makes sense and is polished to a professional standard.

6. Iterative Improvement

In artificial intelligence music production, iterative improvement of the model plays a crucial role in advancing the quality of AI generated music.

By continually updating machine learning models with new music data and user feedback, the generated music continues to get better. This cycle of improvement ensures that each generation of AI assisted music production gets better at creating music we’ll actually listen to.

For music producers and the music industry at large, this means that AI-generated music is not some trend that will come and go, but an evolving tool that has real use cases in the music industry and music production process.

Tools to Get Started with AI Generated Music

Man in front of DJ decks, neon theme

Getting started with AI generated music is easier than ever. There is a range of readily available tools and applications you can play around with online to create AI music at a moment’s notice.

  • Automated Mixing: Employ AI music tools to streamline the mixing and mastering phases, allowing people with little to no experience to create music that sounds professional.

  • Synthetic Voices: Leverage machine learning technologies to produce synthetic voices that mimic specific artists, perfect for cover songs or collaboration concepts.

  • Stem Separation: Utilize AI music generators to isolate tracks into individual stems, allowing for alterations or improvements.

  • Concept Creation: Implement AI to craft prototype songs by blending your compositions with those of renowned artists, optimal for attracting interest from musicians or record labels.

  • AI Music Videos: Explore AI solutions capable of generating music videos, offering time-saving advantages and engaging visual aesthetics to your audience.

As you can see AI generated music isn’t just limited to song generators. There are a range of ways musicians and creators can integrate AI into their music composition workflows!

How Does AI Music Work FAQs

What Types of Music Styles Can AI Music Generators Produce?

From classical to pop, jazz to EDM, AI music generators can create music in a range of styles. However, the quality and authenticity may vary based on the AI algorithm and the data it was trained on.

How Do AI Algorithms Account for Emotional Nuance in Music?

While AI is getting better at understanding musical elements that evoke emotions, it still has limitations compared to the intuitive understanding that human composers have. Advances are being made, but emotional nuance remains a complex factor to encode.

Can AI Music Generators Be Used for Background Music in Commercial Projects?

Most AI music generators offer licensing options that allow their use as background music in commercial projects. However, it’s crucial to read the terms of use to ensure you’re in compliance with licensing requirements.

What Are the Costs Associated with Using AI Music Generators?

While some AI music generators offer free basic versions, premium features often come with a subscription or one-time fee. Costs can vary depending on the complexity and capabilities of the AI tool in question.

Wrapping Up

AI music has moved from a fascinating experiment to a robust tool, fundamentally altering how the music industry approaches the creative process.

From data collection to post-processing, AI technology is streamlining workflows and enabling new music that stands toe-to-toe with human compositions.

As AI in music continues to evolve, the line between human and machine-made music is blurring, paving the way for a new era in the music industry.

Musicians now have the flexibility to focus on refining musical elements, and creating music that resonates with audiences while leveraging AI’s speed and efficiency.

The future of AI music is not just promising; it’s already here. Catch ya! 👋

Similar Posts