AI in Music: Composition, Production, and Analysis

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Authored by Carlos Arana

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Course Code: OLMSC-160

Next semester
starts Jan 12, 2026

12 Weeks

Level 1

Level 1

3-Credit Tuition

$1,575

Artificial intelligence (AI) is rapidly reshaping how music is created, analyzed, produced, and experienced. This course explores the technologies at the core of that transformation, combining technical foundations with hands-on practice. You’ll work with AI tools and workflows through guided activities, independent experimentation, and project-based assignments. Along the way, you’ll tackle tasks in music recognition, analysis, production, and generation, using a range of AI-based apps and software shaping today’s music industry.

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You’ll begin by examining how musical content is represented so it can be processed by AI models, introducing essential concepts in signal processing and Music Information Retrieval (MIR). From there, you’ll explore how AI analyzes, recognizes, classifies, and manipulates musical content, and how these techniques drive intelligent production workflows, such as mixing and mastering. You’ll also examine how AI technologies shape the modern music business, powering data-driven strategies that influence how music is discovered, distributed, and consumed. The course then turns to Generative AI (GenAI), covering algorithmic composition and deep learning models, with tasks such as music and lyric generation, text-to-music systems, style transfer, and voice conversion. The final section addresses the legal, ethical, and societal questions that accompany machine-generated music.

Throughout, you’ll connect each task to the underlying AI methods, investigate commercial tools, and critically evaluate their capabilities.

Over 12 weeks, you’ll learn to harness AI for creative and professional workflows. Emphasizing technological foundations, practical applications, critical analysis, and ethical reflection, this course prepares you to navigate and lead in the evolving world of AI-enhanced music composition, production, and analysis.

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

  • Explain core concepts of artificial intelligence (AI) and their specific applications in music and audio processing.
  • Analyze digital music representations, including symbolic formats (e.g., MIDI) and audio signals.
  • Use real-world AI environments and libraries to analyze audio, visualize musical features, and extract musical content with AI.
  • Apply Music Information Retrieval (MIR) methods to analyze and extract musical features and perform related tasks such as beat detection, chord recognition, and source separation.
  • Examine how AI technologies shape music discovery and distribution, enabling data-driven personalization, playlist curation, trend prediction, and audience segmentation across streaming and social platforms.
  • Experiment with intelligent music production (IMP) tools for tasks such as mixing and mastering.
  • Apply deep learning-based generative AI (GenAI) techniques to create and transform music through tasks such as text-to-MIDI, lyric generation, text-to-music, accompaniment, cover adaptation, and style transfer.
  • Critically evaluate the legal, ethical, and societal implications of AI-generated music.
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Overview Syllabus Requirements Instructors Related Music Career Roles
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Syllabus

Lesson 1: Music Representations for AI: Understanding Musical Content

  • Introduction to Artificial Intelligence (AI) in Music and Audio
  • Music Representations for AI Processing: Symbolic and Audio Signal
  • Audio Representations in Time and Frequency Domains
  • The Collaborative AI Lab: Google Colab and Jupyter Notebooks
  • Music Representations for AI Processing Using Jupyter Notebooks
  • Assignment 1: Music Representations for AI

Lesson 2: Analyzing Musical Features with AI

  • Introduction to Ruled-Based and Expert Systems
  • The Machine Learning (ML) Revolution: Computational Learning Systems
  • The Critical Relationship: Explanatory and Target Variables in ML
  • Deep Learning (DL): The Neural Revolution
  • Audio Features in Signal Processing and Music Analysis
  • Levels of Abstraction in Music Representations and Features
  • Assignment 2: Analysis of AI-Driven Music Applications: Variables and Features

Lesson 3: MIR, Part 1: Recognizing and Extracting Musical Content with AI

  • Introduction to Music Information Retrieval (MIR)
  • Beat Tracking and Tempo Recognition
  • Chord and Key Recognition
  • Deep Learning in MIR: Understanding Image Datasets
  • Chord Recognition with Deep Learning
  • Assignment 3: Beat Detection and Chord Recognition with AI

Lesson 4: MIR, Part 2: Recognizing and Extracting Musical Content with AI

  • Timbre Recognition
  • Instrument Recognition
  • Melody Recognition and Extraction
  • Audio-to-MIDI Conversion (Transcription)
  • Source Separation: Classic and Modern Deep Learning Models
  • Assignment 4: Source Separation with AI

Lesson 5: From MIR to Market: Recognizing, Recommending, and Distributing Music with AI

  • Genre, Sentiment, and Mood Recognition
  • Audio Recognition and Identification
  • Music Recommendation Systems and Personalized Playlist Generation
  • Fan Engagement and Social Media Analytics
  • Emerging Trend Prediction and Playlist Performance Forecasting
  • Marketing Optimization and Audience Targeting
  • Assignment 5: AI in the Music Business

Lesson 6: Mixing with AI: Intelligent Music Production

  • Introduction to Intelligent Music Production (IMP)
  • Adaptive Digital Audio Effects
  • Intelligent Multitrack Mixing
  • Levels of Control in AI Mixing
  • Assignment 6: Mixing with AI

Lesson 7: Mastering with AI: Intelligent Music Production

  • Intelligent Mastering: From Rule-Based and Expert Systems to Deep Learning Approaches
  • Expert Mastering Assistant (EMA)
  • Reference-Based AI Mastering
  • Levels of Control in AI Mastering
  • Assignment 7: Mastering with AI

Lesson 8: Generative AI (GenAI) in Music, Part 1: Algorithmic Composition and Parameter-Based Models

  • Algorithmic Composition Models: Rule-Based, Data-Driven/Probabilistic, and Evolutionary (Genetic) Algorithms
  • Deep Learning for Music Composition: Parameter-Based and Parameter-Free Models
  • Foundations of Deep Music Generation: Recurrent Neural Networks (RNNs)
  • Sequence Modeling and Melody Prediction with RNNs
  • Parameter-Based Models
  • Levels of Control in Parameter-Based Music Generation
  • Assignment 8: Music Generation with AI: Text-to-MIDI

Lesson 9: GenAI in Music, Part 2: Transformers and Large Language Models

  • Foundations of Deep Music Generation: Transformer Models
  • Overcoming Long-Term Coherence Issues in Sequence Modeling with Transformers
  • Large Language Models (LLMs) Principles
  • Embeddings and Vector Semantics in LLMs: Representing Meaning and Context
  • Applications in Songwriting: Lyric Generation and Collaboration
  • Assignment 9: Lyric Generation with AI

Lesson 10: GenAI in Music, Part 3: Parameter-Free Models

  • Foundations of Deep Music Generation: Variational Autoencoders (VAEs) and Diffusion Models
  • Parameter-Free Models: Text-to-Music Models with Prompts
  • High-Level and Fine-Grained Prompting Strategies for Text-to-Music Generation
  • Levels of Control in Parameter-Free Music Generation
  • Strengths and Weaknesses of GenAI Music Models: Finding the Right Balance
  • Assignment 10: Music Generation with AI: Text-to-Music

Lesson 11: GenAI in Music, Part 4: Cover, Extension, Accompaniment, Style Transfer, and RVC

  • Covers: Reinterpretations of Existing Songs in New Styles
  • Extensions: Continuations that Expand Songs With Coherent New Sections
  • Accompaniment: Harmonic and Instrumental Arrangements for Preexisting Melodies or Vocals
  • Style Transfer and RVC: Transforming Musical Pieces and Emulating Different Vocal Timbres
  • Assignment 11: Music Generation with AI

Lesson 12: Ethics, Legal Aspects, and Future Trends in AI Music

  • Copyright and Ownership Challenges in AI-Generated Music
  • Legal Frameworks and Human vs. AI Copyright
  • Dataset Curation and GenAI Music Model Training Using Copyrighted Works
  • Consent and Compensation Frameworks for Composers Whose Songs Are Used in GenAI Training
  • Bias and Representation: Addressing Discrimination in AI Music Systems
  • Creative and Cultural Impact: Balancing AI Innovation with Human Artistry and Expression
  • Future Trends in AI Music
  • Assignment 12: Culminating Experience: Comprehensive Application of AI in Music

Requirements

Prerequisites and Course-Specific Requirements 

Prerequisite Courses, Knowledge, and/or Skills
Students should have:

  • A basic, working knowledge of music production, including working with a DAW
  • A basic, working knowledge of songwriting (song characteristics, genre, groove and mood, what a drum pattern is, the difference between melody and accompaniment, and what a chord progression is)
  • A curious mindset and the desire to think critically about how AI will influence and enhance many aspects of music-related activities and the music industry as a whole

Note: No prior programming experience is required. You’ll use real-world AI environments and libraries to analyze audio, visualize musical features, and extract musical content.

Media and Subscriptions

  • Chartmetric subscription (Free plan sufficient)

Software

  • Full-featured Digital Audio Workstation (DAW), such as Pro Tools (Studio or Ultimate), Logic Pro, Cubase Pro, Ableton Live (Suite or Standard), Reaper, Reason, or FL Studio (Producer or Signature). Note that GarageBand is not acceptable.
  • Moises (Premium or higher)
  • iZotope Neutron 4 or higher (Elements edition acceptable)
  • Chordify (Basic plan sufficient)
  • Automix by Roex (Free plan sufficient)
  • AIVA (Free plan sufficient)
  • Suno (Pro plan or higher)
  • Audimee (Free plan sufficient)
  • Large language model-based chatbot, such as ChatGPT, Gemini, or Claude (free versions sufficient)
  • Each of the following applications are required and will be introduced within the course:
  • Word processing software, such as Microsoft Word, Google Docs (free), Apple Pages, Apache OpenOffice (free), etc.

Student Deals
After enrolling, be sure to check out our Student Deals page for various offers on software, hardware, and more. Please contact support@online.berklee.edu with any questions.

General Course Requirements

Below are the minimum requirements to access the course environment and participate in Live Classes. Please make sure to also check the Prerequisites and Course-Specific Requirements section above, and ensure your computer meets or exceeds the minimum system requirements for all software needed for your course. 

Mac Users

PC Users

All Users

  • Latest version of Google Chrome
  • Zoom meeting software
  • Webcam
  • Speakers or headphones
  • External or internal microphone
  • Broadband Internet connection

Instructors

Carlos Arana
Carlos Arana

Author & Instructor

Carlos “Charly” Arana is a guitarist, producer, and researcher. His specialties range from Latin American rhythms to the application of machine learning and artificial intelligence techniques for music. As a guitarist, arranger, and musical director he has worked and recorded with artists from all over the world, including legendary Bossa Nova singer Maria Creuza, whose band he was a member of. He has edited a number of books for Hal Leonard and Warner Bros. Publications (Alfred Publications), and for his studies and research on machine learning and AI he has been invited as a speaker in congresses and seminars at some of the most prestigious technology universities, such as MIT and UC Berkeley.

What's Next?

When taken for credit, AI in Music: Composition, Production, and Analysis can be applied towards the completion of these related programs:

Related Certificate Programs

Related Music Career Roles

Employers look for skills learned in this course, when hiring for the following music career roles:

Composer (Video Games)

Interactive Media Specialist

Product Manager

Songwriter-Producer

Sound Designer (Games and Tech)

Questions?

Contact our Academic Advisors by phone at 1-866-BERKLEE (U.S.), 1-617-747-2146 (INT'L), or by email at advisors@online.berklee.edu.

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