AI for Music and Audio

In this course, you’ll delve into the core principles of AI and its diverse applications in music and audio. Through hands-on activities, you’ll gain experience with AI models and algorithms and have the opportunity to explore the range of AI-based apps and software available in the market.

This course is designed to integrate the use of AI on the different stages of a musical work lifecycle. You’ll start by coming to an understanding of musical content for AI. First, you’ll learn about the fundamentals of AI models and architectures, and related terminology. Then, we move to examining how musical content is represented for then being processed by AI models/architectures. For that, we delve into the basics of Signal Processing and Music Information Retrieval (MIR), which is crucial for understanding the nuances of musical content.

Then we’ll focus on practical applications as you work on extracting and recognizing musical content through AI. This encompasses rhythm detection, melody extraction, chord detection, and source separation, exploring both the theoretical framework and practical apps used for these tasks.

In the next stage, we’ll explore processing musical content using AI, and discover all of the ways in which AI is applied to music production, mixing, and mastering. You’ll learn about technological advancements in AI that have enhanced music production tasks and the practical application of these techniques using industry-leading AI tools and commercial apps. We’ll also get into AI's transformative potential in the distribution and consumption of musical works. We’ll examine AI’s impact on processes like genre classification, music recommendation, playlist creation, and audio identification.

The final stage focuses on creating musical content with AI. You’ll delve into generative AI, covering the evolution, principles, and applications of AI in music composition. You’ll learn about various neural network architectures, and their role in creating original musical content, as well as the most used apps in the market for music generation. This lesson not only teaches the technical aspects of generative AI but also addresses the legal and ethical challenges in AI-generated music.

We will wrap up the course by presenting and analyzing different alternatives and creative workflows, utilizing a variety of tools and apps discussed in the course, with its distinct types and functionalities. This includes focusing on the features of each tool and app for specific parts of the composition and/or arrangement process, ensuring a comprehensive understanding of how these technologies can enhance musical creativity.

No coding or programming experience is needed for this course. You’ll be provided with Python code and libraries for understanding the behind-the-scenes procedures used in AI models and apps.

This course will encourage you to experiment with AI technologies at key stages of a musical work lifecycle, from creation to production and consumption, which will allow you to increase the productivity of your work, stimulate creativity, and enhance your musical projects.

By the end of the course, you will be able to:

• Describe the fundamental concepts of AI and the various types of models used for tasks in music analysis and processing.
• Discuss the technological background behind AI techniques, such as source and vocal separation, melody extraction, chord recognition, and implement them using available apps and software in the market.
• Grasp the fundamental principles and practical skills required for AI-driven music production, particularly in intelligent mixing and mastering, while exploring the transformative impact of these innovations on music production workflows.
• Identify the key components and AI-based technologies behind song feature extraction, genre classification, recommendation, and personalization systems, recognizing their pivotal role in enhancing music listening experiences.
• Evaluate diverse generative AI neural network architectures used in music composition, including their strengths and weaknesses.
• Describe the different techniques used by GenAI models to assist the composition and songwriting processes.
• Gain practical proficiency in leveraging commercial AI tools and apps to enhance creative music composition processes and workflows.
• Develop a critical mindset for assessing the effectiveness, usefulness, and quality of apps and software used for various applications of AI in music and audio.
• Discuss the complexities and copyright challenges associated with the authorship of GenAI musical works and the training of the deep learning models backing these apps.