Guest lecture for Music 364, CCRMA, Stanford University, with Blair Kaneshiro.
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README.md

stanford-music-364

Guest lecture for Music 364, CCRMA, Stanford University, with Blair Kaneshiro.

Title: Feature Extraction and Classification of Musical Signals

Date: 2017 January 27, 12:30-2:20 pm

Music 364 Course Website

Goals

  1. Address the broad question: how do we teach computers to understand music?
  2. Inspect some specific tasks in music information retrieval (MIR):
    • genre classification
    • song structure and form
  3. Write basic systems in Python to accomplish these MIR tasks.

Outline

  1. Audio Representation
    • Time Domain
    • Frequency Domain
  2. Segmentation
  3. Feature Extraction
    • zero-crossing rate
    • spectral moments (centroid, etc.)
  4. Feature Analysis with Pandas
  5. Genre Classification
    • brief literature review of existing approaches
    • K-NN using scikit-learn
  6. Song Structure/Form
    • brief literature review of existing approaches
    • relevant features, e.g. chroma, MFCCs
    • K-means clustering using scikit-learn

Homework

  1. Feature Sonification
  2. Genre Classification

Software Prerequisites

Git is not required for this lecture and assignment. However, those who do know Git may find it convenient to clone this repository onto their computers.

IPython, scikit-learn, pandas

Install Python 2 and relevant libraries. Your system may already have Python 2.

If you’re totally new, the simplest solution is to download and install Anaconda for Python 2 (2.7), not Python 3. If you can do the following without errors, then you’re set:

  1. Run the IPython shell.

    • For Mac, at the Terminal: ipython.
    • For Windows, open the application "IPython".

    Type exit to exit.

  2. In the IPython shell, run import scipy, sklearn, pandas. If that runs without error, congratulations.

  3. Run the IPython/Jupyter notebook.

    • For Mac, at the Terminal: jupyter notebook.
    • For Windows, open the application "Jupyter Notebook". Alternatively for Windows: open the application "Anaconda Prompt" and type in jupyter notebook.

    To close the IPython notebook,

    1. Save the notebook. (Either use keyboard shortcut s, or "File | Save" in the menu.)
    2. Close the window.
    3. If you opened the notebook from a prompt/shell, press <Ctrl-C> twice to return to the prompt.

Immediately after installing, if something doesn’t work, try closing the terminal or restarting the OS. Sometimes that can reset the necessary configurations.

Librosa

Librosa is a Python library for audio and music analysis.

  1. Install Librosa. In short:

    pip install librosa
    

    Mac users will enter this command in the Terminal; Windows users will enter this command from the Anaconda Prompt. If that doesn't work, try:

    conda install -c conda-forge librosa
    
  2. Follow the tutorial quickstart. If you can execute these lines in particular:

    import librosa
    filename = librosa.util.example_audio_file()
    x, fs = librosa.load(filename)
    

    then you are in good shape.

More information on Librosa: