Jump to Navigation

Main menu

  • Login
Home

Secondary menu

  • [Room Booking]
  • [Wiki]
  • [Webmail]

Deep Learning for Music Information Retrieval

Workshop Date: 
Mon, 07/31/2017 - Fri, 08/04/2017

Tuition scholarships available for applicants commited to advancing diversity in STEM. Fill out this form by June 2nd.


Instructors: Irán Román
TAs: Ankita Mitra & Anish Nag


The availability of large-scale databases has facilitated recent advances in Deep Learning across fields like computer vision, genomics, and natural language processing. These techniques are also applied in the field of Music Information Retrieval.

We will master the theory behind tools at the intersection of machine learning, Digital Signal Processing, Music Information Retrieval, and Computational Neuroscience. First we will write software completely from scratch, and then we will optimize our implementations with TensorFlow.

Syllabus:
Day 1: Feature spaces of music. Review of k-means clustering, SVMs, and regression classifiers.
Day 2: Decomposing and building a Softmax Neural Network from scratch, and training it with music.
Day 3: Obtaining Spectral Features from raw audio using Convolutional Neural Networks.
Day 4: Natural Language Processing in Music using Recurrent Neural Networks.
Day 5: Make it your own: Proposing and developing a Final Project with Neural Networks.

Prerequisites:
- CCRMA MIR workshop (any year) or consent from instructor (iran@ccrma_DOT_stanford_DOT_edu).
- Calculus and programming experience with Python
- Recommended: Linear Algebra.

About the instructor: Irán Román studies Computer-based Music Theory at Stanford University's CCRMA (Center for Computer Research in Music and Acoustics), and Computational Neuroscience at the Stanford Neuroscience Institute, carrying out research with Dr. Takako Fujioka. He is also a student in the graduate training program at the Stanford Center for Mind Brain and Computation, carrying out research with Dr. Jay McClelland.

IMPORTANT: Contact the instructor before registering to confirm your eligibility. Attach a copy of your registration or diploma for the CCRMA Music Information Retrieval workshop taught by Steve Tjoa. Describe your experience with python programming (preferably include a link to your github page), and college-level math classes at the level of Calculus I or above.



  • Home
  • News and Events
    • All Events
      • CCRMA Concerts
      • Colloquium Series
      • DSP Seminars
      • Hearing Seminars
      • Guest Lectures
    • Event Calendar
    • Events Mailing List
    • Recent News
  • Academics
    • Courses
    • Current Year Course Schedule
    • Undergraduate
    • Masters
    • PhD Program
    • Visiting Scholar
    • Visiting Student Researcher
    • Workshops 2023
  • Research
    • Publications
      • Authors
      • Keywords
      • STAN-M
      • Max Mathews Portrait
    • Research Groups
    • Software
  • People
    • Faculty and Staff
    • Students
    • Alumni
    • All Users
  • User Guides
    • New Documentation
    • Booking Events
    • Common Areas
    • Rooms
    • System
  • Resources
    • Planet CCRMA
    • MARL
  • Blogs
  • Opportunities
    • CFPs
  • About
    • The Knoll
      • Renovation
    • Directions
    • Contact

Search this site:

Fall Courses at CCRMA

Music 101 Introduction to Creating Electronic Sounds
Music 192A Foundations in Sound Recording Technology
Music 201 CCRMA Colloquium
Music 220A Foundations of Computer-Generated Sound
Music 223A Composing Electronic Sound Poetry
Music 256A Music, Computing, and Design I: Software Paradigms for Computer Music
Music 319 Research Seminar on Computational Models of Sound Perception
Music 320 Introduction to Audio Signal Processing
Music 351A Research Seminar in Music Perception and Cognition I
Music 423 Graduate Research in Music Technology
Music 451A Auditory EEG Research I

 

 

 

   

CCRMA
Department of Music
Stanford University
Stanford, CA 94305-8180 USA
tel: (650) 723-4971
fax: (650) 723-8468
info@ccrma.stanford.edu

 
Stanford Digital Accessibility
Web Issues: webteam@ccrma
site copyright © 2009-2023
Stanford University

site design: 
Linnea A. Williams