356-winter-2023/hw2

Programming Project #2: "Featured Artist"

Music and AI (Music356/CS470) | Winter 2023 | by Ge Wang

In this programming project, we will learn to work with audio features for both supervised and unsupervised tasks. These include a real-time genre-classifier and a feature-based audio mosaic tool using similarity retrieval. Create a feature-driven musical statement or performance!

Due Dates

• Milestone: webpage due Wednesday (2/1, 11:59pm) | in-class critique Thursday (2/2)
• Final Deliverable: webpage due Wednesday (2/8, 11:59pm)
• In-class Presentation: Thursday (2/9)

Discord Is Our Friend

• direct any questions, rumination, outputs/interesting mistakes to our class Discord

Things to Think With

• read/skim the classic article "Musical Genre Classification of Audio Signals" (Tzanetakis and Cook, 2002)
  • don't worry about the details yet; first get a general sense what audio features and how they can be used

Tools to Play With

• get the latest bleeding edge secret chuck build (2023.01.23 or later!)
  • macOS this will install both command line chuck and the graphical IDE miniAudicle, and replace any previous ChucK installation.
  • Windows you will need to download and use the bleeding-edge command line chuck (for now, there is no bleeding-edge miniAudicle for Windows); can either use the default cmd command prompt, or might consider downloading a terminal emulator.
  • Linux you will need to build from source, provided in the linux directory
  • all platforms for this project, you will be using the command line version of chuck.
• NOTE: to return your chuck back to a pre-bleeding-edge state, you can always install the latest official ChucK release

GTZAN Dataset

• next, you'll need to download the GTZAN dataset
  • 1000 30-second music clips, labeled by humans into ten genre categories

Phase One: Feature Extract, Classify, Validate

• understanding audio, FFT, feature extraction
• extract different sets of audio features from GTZAN dataset
• run real-time classifier using different feature sets
• run cross-validation to evaluate the quality of classifier based different features

HW2 Sample Code

• you can find sample code here
• start playing with these, and reading through these to get a sense of what the code is doing
  • example-centroid.ck
  • example-mfcc.ck
  • feature-extract.ck
  • genre-classify.ck
  • x-validate.ck

Phase Two: Curate Feature Database, Design Audio Mosaic Tool

• build a database mapping sound frames (100::ms to 1::second) <=> feature vectors
  • curate your own set of audio files can be mixture of
    • short sound effects (~1 second)
    • music (we will perform feature extraction on each short-time window)
• prototype a feature-based sound explorer to query your database and perform similarity retrieval
• using your database and retrieval tool, design an interactive audio mosaic generator
  • feature-based
  • real-time
  • takes any audio input (mic or any unit generator)
  • can be used for performance
• (optional) do this in the audiovisual domain

Phase Three

• • use your prototype from Phase Two to create a musical statement
  • (optional) do this in the audiovisual domain

Reflections

• write ~300 words of reflection on your project. It can be about your process, or the product. What were the limitations (and how did you try to get around them?)

Deliverables

• create a CCRMA webpage for this etude
  • the URL should live at https://ccrma.stanford.edu/~YOURUSERID/356/hw2 or https://ccrma.stanford.edu/~YOURUSERID/470/hw2
  • alternately, you may use Medium or another publishing platform (but please still link to that page from your CCRMA webpage)
• your webpage is to include
  • a title and description of your project (free free to link to this wiki page)
  • all relevant chuck code from all three phases
    • phase 1: all code used (extraction, classification, validation)
    • phase 2: your mosaic generator, and database query/retrieval tool
    • phase 3: code used for your musical statement
  • video recording of your musical statement (please start early!)
  • your 300-word reflection
  • any acknowledgements (people, code, or other things that helped you through this)
• submit to Canvas only your webpage URL