AI in Music: Composition, Production, and Analysis

Carlos Arana

Authored by Carlos Arana

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Course Code: OLMSC-160

Explore the key technologies at the core of music and AI, combining technical foundations with hands-on practice. In this undergraduate music production course, 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.

Level 1
Foundational
Modality
Online
Duration
12 Weeks
3-Credit Tuition
$1,575
Semester Starts
June 29
Accreditation
NECHE

Key Learning Outcomes

  • Apply AI techniques to analyze, generate, and transform music using real-world tools and workflows
  • Use Music Information Retrieval (MIR) methods to extract musical features such as rhythm, harmony, and sources from audio
  • Experiment with intelligent music production tools to support mixing, mastering, and creative decision-making
  • Evaluate the ethical, legal, and societal implications of AI-generated music in contemporary creative and industry contexts
Overview Syllabus Requirements Instructors Related Music Career Roles
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Course Description

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 this 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
  • 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
  • Analyze symbolic and audio-based music representations to understand how musical features are encoded, extracted, and used in AI systems
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Syllabus

Lesson 1: Music Representations for AI: Understanding Musical Content

  • Introduction to Artificial Intelligence and Its Role in Music
  • Music Representations for AI Processing
  • Symbolic Music Representations
  • Audio Signal Representations
  • Audio Representations in Time and Frequency Domains
  • Time-Domain Representations
  • Understanding Musical Sounds: Fundamentals and Harmonics
  • Time-Frequency Representations
  • Spectrograms
  • The Collaborative AI Lab: Google Colab and Jupyter Notebooks
  • Practice Exercise: Music Representations for AI Processing Using Jupyter Notebooks
  • Assignment 1: Music Representations for AI

Lesson 2: Analyzing Musical Features with AI

  • The Evolution of AI: From Rules to Deep Learning
  • The Machine Learning Revolution: From Data to Decisions
  • The Cognitive Framework of Machine Learning
  • The Critical Relationship: Explanatory and Target Variables in Machine Learning
  • Deep Learning: The Evolution of Representation Through Hierarchical Layers
  • Generative AI: Key Architectures
  • Audio Features in Signal Processing and Music Analysis
  • Levels of Abstraction in Music Features: Bridging the Semantic Gap
  • Assignment 2: Designing a Machine Learning System for a Music Task

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

  • Introduction to Music Information Retrieval (MIR)
  • Beat Tracking
  • Novelty Curve: Visual Representation of Rhythmic Changes
  • Spectral-Based Analysis: Understanding the Frequency Dimension of Music
  • Practice Exercise: Beat Tracking
  • Chord and Key Recognition
  • Chroma Features: The Foundation of Harmonic Analysis
  • Chromagram: A Digital Translation of Chroma Perception
  • Chord Recognition Techniques
  • Key Detection Algorithms
  • Deep Learning in MIR: How Image Datasets Become Training Data
  • Deep Learning in MIR: How Audio Becomes Data
  • Deep Learning for Chord Recognition
  • The Critical Role of Data Diversity in Deep Learning
  • Assignment 3: 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)
  • Practice Exercise: Audio-to-MIDI Conversion
  • Source Separation
  • Traditional Approaches to Source Separation: Binary Masking
  • Modern Approaches to Source Separation: Deep Learning
  • Foundation Models and Performance
  • Common Errors and Challenges in AI-Based Source Separation
  • Assignment 4: Source Separation with AI

Lesson 5: MIR in the Music Business: Recognizing, Recommending, and Distributing Music with AI

  • Genre Recognition
  • Machine Learning in Genre Recognition
  • Deep Learning Approaches to Genre Recognition
  • Mood Recognition
  • Audio Recognition and Identification
  • Signal Transformation and Constellation Map Matching
  • Challenges and Applications of Audio Recognition
  • Intelligent Music Discovery
  • Music Recommendation Systems: Two Primary Approaches
  • From Recommendations to Curated Experiences: Personalized Playlist Generation
  • AI in the Music Business: From Discovery to Strategy
  • Mini Scenarios: From Music Data to Business Decisions
  • Assignment 5: Personalization, Discovery, and Music Strategy

Lesson 6: Mixing with AI: Intelligent Music Production

  • Intelligent Music Production (IMP) and Mixing
  • Adaptive Digital Audio Effects
  • Adaptive Processing Systems
  • Constraints in Adaptive Processing
  • Intelligent Multitrack Mixing
  • Deep Learning for Multitrack Mixing
  • Commercial Implementations of AI-Based Multitrack Mixing
  • Levels of Control in Intelligent Music Production
  • Insightful Control in AI Mixing
  • Suggestive Control in AI Mixing
  • Independent (or Automatic) Control in AI Mixing
  • Finding the Right Balance
  • Assignment 6: IMP – Single and Multitrack Processing

Lesson 7: Mastering with AI: Intelligent Music Production

  • AI in Mastering
  • Unsupervised Learning and Style Templates (Clustering)
  • Hybrid Systems: The Expert Mastering Assistant (EMA)
  • Reference-Based Mastering
  • Deep Learning Approaches to Mastering
  • Evaluating and Deploying AI Mastering Systems
  • Levels of Control in AI Mastering
  • Insightive Control in AI Mastering
  • Suggestive Control in AI Mastering
  • Independent (or Automatic) 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
  • Rule-Based Models: Generative Grammars
  • Data-Driven Probabilistic Models: Markov Chains
  • Evolutionary (Genetic) Algorithms
  • Deep Learning Architectures for Music Composition
  • Foundations of Deep Music Generation: Recurrent Neural Networks (RNNs)
  • Training RNNs for Melody Prediction
  • Parameter-Based Models
  • Levels of Control in Deep Music Generation
  • Semantic Control: Aesthetic Intention and Overall Style
  • Musical Control: Direct Specification of Musical Structure and Elements
  • Meta-Generative Control: Variations and Sampling Parameters
  • Levels of Control in Text-to-MIDI Models
  • The Supervisory Role in Deep Music Generation
  • Assignment 8: Music Generation with AI – Text-to-MIDI

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

  • Transformers and Large Language Models
  • Vector Semantics and Embeddings
  • Lyric Writing as Prompt Design
  • Process: Staging the Task
  • Semantic: From Broad Regions to Specific Meaning
  • Structural: Voice and Section Coherence
  • Practical Iterative Workflow: Using LLMs as a Controlled Creative Partner
  • Why Iterative Prompting Works
  • Text, Structure, and Generative Music Systems
  • Bias and Cultural Representation
  • Assignment 9: Lyric Writing with AI

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

  • Text-to-Music AI Models: Training and Generating Music
  • Review: Large Language Models and Embedding Spaces
  • Cross-Modal Learning: Aligning Text and Music
  • MuLan: The Shared Text–Audio Embedding Space
  • w2v-BERT: From Semantic Meaning to Musical Structure
  • SoundStream: Rendering the Sound
  • The Full MusicLM Architecture
  • Text-to-Music Prompting: From Architecture to Creative Practice
  • A First Generation: Why Generic Prompts Produce Generic Music
  • From Text to Sound: The Generation Interface and the Style Prompt
  • Metatags: Controlling Structure from Inside the Lyrics
  • Negative Prompting: Defining Boundaries in Semantic Space
  • The Unified Principle: Constraint as More Defined Regions in Semantic Space
  • Assignment 10: Music Generation with AI – Text-to-Music

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

  • Diffusion Models and Audio Re-rendering
  • Targeted Regeneration
  • AI Cover Generation
  • AI-Generated Accompaniment
  • Style Transfer and Voice Conversion
  • Foundational Technology: Retrieval-Based Voice Conversion (RVC)
  • Ethics and Legal Aspects of Retrieval-Based Voice Conversion (RVC)
  • Assignment 11: Music Transformation with AI

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

  • Legal Foundations: Copyright and Authorship
  • Training Data
  • Voice, Identity, Transparency, and Disclosure
  • Future Trends in AI Music
  • Closing Reflection: What This Course Has Prepared You For
  • 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

  • macOS Monterey 12.0 or later

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 Degree Majors

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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