Deep Learning for MIR (TWO WEEKS)

The Stanford Center for Computer Research in Music and Acoustics (CCRMA) is a multi-disciplinary facility where composers and researchers work together using computer-based technology both as an artistic medium and as a research tool.Pronouncing "CCRMA": CCRMA is an acronym for the Center for Computer Research in Music and Acoustics it is pronounced "karma" (the first "c" is silent).Areas of ongoing interest:CompositionApplications HardwareApplications SoftwareSynthesis Techniques and AlgorithmsPhysical ModelingP﻿hysical Interaction Design for MusicMusic and Mobile DevicesSensorsReal-Time ControllersSignal ProcessingDigital Recording and EditingPsychoacoustics and Musical AcousticsPerceptual Audio CodingMusic Information RetrievalAudio NetworkingAuditory Display of Multidimensional Data (Data Sonification)Real-Time Applications.The CCRMA community:Administrative and technical staff, faculty, research associates, graduate research assistants, graduate and undergraduate students, visiting scholars, visiting researchers and composers, and industrial associates. Departments actively represented at CCRMA include Music, Electrical Engineering, Mechanical Engineering, Computer Science, Physics, Art, Drama, and Psychology.Center activities:Academic courses, seminars, small interest group meetings, summer workshops and colloquia. Concerts of computer music are presented several times each year, including exchange concerts with area computer music centers. In-house technical reports and recordings are available, and public demonstrations of ongoing work at CCRMA are held periodically.Research results:Are published and presented at professional meetings, international conferences and in established journals including the Computer Music Journal, Journal of the Audio Engineering Society, the Journal of the Acoustical Society of America, and various transactions of the Institute of Electrical and Electronic Engineers (IEEE). Compositions are presented in new music festivals and radio broadcasts throughout the world and have been recorded on cassette, LP, compact disc, and in the cloud.

CCRMA Summer Workshops

Music Information Retrieval, starting with basics, and ending with state-of-the-art algorithms.

The Knoll

<h3>Deep Learning for Music Information Retrieval</h3><h3> </h3>This workshop offers a fast-paced introduction to audio and music processing with deep learning to bring you up to speed with the state-of-the-art practice in 2025. Participants will learn to build tools to analyze and manipulate digital audio signals with PyTorch. Both theory and practice of digital audio processing will be discussed with hands-on exercises on algorithm implementation. These concepts will be applied to various topics in music information retrieval. Some knowledge of python, linear algebra, and object oriented programming are assumed.In-person (CCRMA, Stanford) and online enrollment options available. Students will receive the same teaching materials and have access to the same tutorials in either format. However, students will gain access to more in-depth, hands-on 1:1 instructor discussion and feedback when taking the course in-person. <h3>Schedule</h3>Day 1 • Review: Fundamentals of audio signals, key mathematical concepts (linear algebra, calculus), and common music/audio features (MFCCs, chroma, spectral contrast). • Theory: Overview of time-frequency representations (STFT, mel-spectrogram), feature extraction pipelines. • Hands-on: Audio feature extraction using Librosa and TorchAudio. Day 2 • Review: Feedforward neural networks and the fundamentals of deep learning (backpropagation, loss functions). • Theory: Introduction to the Transformer architecture; comparison with traditional sequence models. • Hands-on: Training a simple Transformer for sequence classification (e.g., audio command recognition). Day 3 • Theory: Convolutional Neural Networks (CNNs) for audio classification; Recurrent Neural Networks (RNNs) for temporal modeling. • Hands-on: Spectrogram-based genre or instrument classification using CNNs and/or RNNs in PyTorch. Day 4 • Theory: Generative models for audio — Variational Autoencoders (VAEs), diffusion models, and their applications in audio/music synthesis. • Hands-on: Musical tone generation using a pitch- or timbre-conditioned VAE; exploration of a pre-trained diffusion model for audio generation. Day 5 • Literature: Guided reading and discussion on recent papers (e.g., AudioCLIP, Jukebox, AudioLM, MusicLM, MusicGen). • Hands-on: Group project presentations and demos (e.g., semantic audio tagging, music synthesis, or creative audio applications using models explored during the week). About the instructorsKitty Shi is an accordionist, pianist, bagpipes player, and a music technologist. She received her PhD from CCRMA in 2021 and she’s now a machine learning engineer at Pinterest. Kitty’s research interest is in computer-assisted expressive musical performance.Iran R. Roman is a faculty member at Queen Mary University London, leading research in theoretical neuroscience and machine perception. He holds a PhD from CCRMA. Iran is a passionate instructor and mentor, with extensive experience teaching AI and signal processing at institutions like Stanford University, New York University, and the National Autonomous University of Mexico. He has worked with companies companies like Plantronics, Apple, Oscilloscape, Tesla, and Raytheon/BBN to build and deploy AI models. iranroman.github.io

Deep Learning for MIR (TWO WEEKS)

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Deep Learning for MIR (TWO WEEKS)

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