Introduction

1. About This Site (ipynb) Start here!
2. What is MIR? (ipynb)
3. Python Basics (ipynb)
4. Jupyter Basics (ipynb)
5. Jupyter Audio Basics (ipynb)
6. NumPy and SciPy Basics (ipynb)
7. Alphabetical Index of Terms (ipynb)

About This Site

musicinformationretrieval.com is a collection of instructional materials for music information retrieval (MIR). These materials contain a mix of casual conversation, technical discussion, and Python code.

These pages, including the one you’re reading, are authored using Jupyter notebooks. They are statically hosted using GitHub Pages. The GitHub repository is found here: stevetjoa/stanford-mir.

This material is used during the annual Summer Workshop on Music Information Retrieval at CCRMA, Stanford University. Click here for workshop description and registration.

This site is maintained by Steve Tjoa. For questions, please email steve@stevetjoa.com. Do you have any feedback? Did you find errors or typos? Are you a teacher or researcher and would like to collaborate? Please let me know.

What is Music Information Retrieval?

While you listen to these excerpts, name as many of its musical characteristics as you can. Can you name the genre? tempo? instruments? mood? time signature? key signature? chord progression? tuning frequency? song structure?

What is MIR?

Here is a sampling of tasks found in music information retrieval:

• fingerprinting
• cover song detection
• genre recognition
• transcription
• recommendation
• symbolic melodic similarity
• mood
• source separation
• instrument recognition
• pitch tracking
• tempo estimation
• score alignment
• song structure/form
• beat tracking
• key detection
• query by humming

Why MIR?

• discover, organize, monetize media collections
• search (“find me something that sounds like this”) songs, loops, speech, environmental sounds, sound effects
• workflows in consumer products through machine hearing
• automatic control of software and mobile devices

How is MIR done?

Well, that’s a big question. Two primary areas in music analysis include tonal analysis (e.g. melody and harmony) and rhythm and tempo (e.g. beat tracking). Here are some great overviews by Meinard Müller (author, FMP) on both topics.

Python Basics

Why Python?

Python is a general-purpose programming language that is popular and easy to use. For new programmers, it is a great choice as a first programming language. In fact, more and more university CS departments are centering their introductory courses around Python.

For a summary of reasons to move from Matlab to Python, please read this post.

This page on Udacity provides some more great reasons to use Python, along with resources for getting started.

Jupyter Basics

You are looking at a Jupyter Notebook, an interactive Python shell inside of a web browser. With it, you can run individual Python commands and immediately view their output. It’s basically like the Matlab Desktop or Mathematica Notebook but for Python.

To start an interactive Jupyter notebook on your local machine, read the instructions at the GitHub README for this repository.

If you are reading this notebook on http://musicinformationretrieval.com, you are viewing a read-only version of the notebook, not an interactive version. Therefore, the instructions below do not apply.

Jupyter Audio Basics

Audio Libraries

We will mainly use two libraries for audio acquisition and playback:

1. librosa

librosa is a Python package for music and audio processing by Brian McFee. A large portion was ported from Dan Ellis’s Matlab audio processing examples.

2. IPython.display.Audio

IPython.display.Audio lets you play audio directly in an IPython notebook.

NumPy and SciPy

The quartet of NumPy, SciPy, Matplotlib, and IPython is a popular combination in the Python world. We will use each of these libraries in this workshop.

Alphabetical Index of Terms