musicinformationretrieval.com

stanford-mir is now musicinformationretrieval.com.

Instructional material for the Music Information Retrieval Workshop at CCRMA, Stanford University, 2014-18.

The Stanford CCRMA MIR 2018 Workshop has come to an end. This year's esteemed instructors included:

Meinard Müller (Int'l Audio Laboratories Erlangen; author, Fundamentals of Music Processing)
Brian McFee (NYU; creator, librosa).

How to Use This Repo

This repository contains Jupyter notebooks related to music information retrieval (MIR). Inside these notebooks are Python code snippets that illustrate basic MIR systems.

The simplest way to use this repository is to (1) browse a read-only version of this repo at musicinformationretrieval.com, and (2) follow along using a blank Jupyter notebook of your own.

Install Python and relevant libraries

If you’re totally new, the simplest solution is to download and install Anaconda for Python 3.x.
Install librosa and ffmpeg. If you installed Anaconda during step 1:
```
conda install -c conda-forge librosa ffmpeg
```

If these packages are already installed, don't forget to upgrade them before the workshop.

To upgrade, use either pip install --upgrade or conda upgrade depending upon which package installer you used for the respective package.

[2018 June 24] These notebooks reflect the following package versions:

numpy 1.14.2
scipy 1.0.0
matplotlib 2.2.0
ipython 6.2.1
jupyter 1.0.0
librosa 0.6.1
scikit-learn 0.19.1
pandas 0.22.0
joblib 0.11

Troubleshooting

Issue #729: import librosa causes TypeError: expected string or buffer

Workaround: downgrade joblib to v0.11:
```
pip install joblib==0.11
```

Start the Jupyter Notebook

A majority of our instruction and laboratory work will occur inside the Jupyter notebook. You can think of the Jupyter notebook like the Mathematica or Matlab desktop, except it uses Python and it runs inside your web browser.

Start the Jupyter notebook server on your local machine. For Mac users, at the Terminal:
```
your-local-machine:~$ jupyter notebook
```
For Windows users, open the application "Jupyter Notebook". Alternatively for Windows: open the application "Anaconda Prompt" and type in jupyter notebook.
On your local machine, visit http://localhost:8888 inside a web browser.
To open a new notebook, click on New near the top right. This is your scrap notebook.
- Immediately rename the notebook by clicking on "Untitled" in the top left, and choosing a different name.
- Inside a cell, run 1+2. Press <Shift-Enter> on a cell to run that cell. Hopefully you get the output 3.
- Inside a cell, run import scipy, sklearn, pandas, librosa. Press <Shift-Enter> to run the cell. If that runs without error, congratulations, you have the necessary libraries installed properly.
- Try executing the content from http://musicinformationretrieval.com inside this blank notebook.
To close the Jupyter notebook,
- Save the notebook. (Either use keyboard shortcut s, or "File | Save" in the top menu.)
- Close the browser window.
- If you opened the notebook from a prompt/shell as indicated in Step 1 above, from that shell, press <Ctrl-C> twice to return to the prompt.

Congratulations, you are now running a Jupyter notebook, and you can get started with the workshop material.

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

Contributions

Your contributions are very welcome! You can contribute in two ways:

Submit an issue. Click on "Issues" in the right navigation bar, then "New Issue". Issues can include Python bugs, spelling mistakes, broken links, requests for new content, and more.
Submit changes to source code or documentation. Fork this repo, make edits, then submit a pull request.

This repo is statically hosted using GitHub Pages. Any changes to HTML files in the gh-pages branch will be seen on musicinformationretrieval.com.

To edit musicinformationretrieval.com:

Edit a notebook, e.g.:
```
$ jupyter notebook kmeans.ipynb
```

Convert notebook to HTML:

$ jupyter nbconvert --to html kmeans.ipynb

Commit the notebook and the HTML:
```
$ git add kmeans.ipynb kmeans.html
$ git commit
$ git push
```
You may need to wait 1-2 minutes before the changes are live on GitHub Pages.

Name		Name	Last commit message	Last commit date
Latest commit History 354 Commits
audio		audio
img		img
.gitignore		.gitignore
CNAME		CNAME
LICENSE		LICENSE
README.md		README.md
about.html		about.html
about.ipynb		about.ipynb
about_ccrma_workshop.html		about_ccrma_workshop.html
about_ccrma_workshop.ipynb		about_ccrma_workshop.ipynb
about_fmp.html		about_fmp.html
about_fmp.ipynb		about_fmp.ipynb
adtlib.html		adtlib.html
adtlib.ipynb		adtlib.ipynb
alphabetical_index.html		alphabetical_index.html
alphabetical_index.ipynb		alphabetical_index.ipynb
audio_representation.html		audio_representation.html
audio_representation.ipynb		audio_representation.ipynb
autocorrelation.html		autocorrelation.html
autocorrelation.ipynb		autocorrelation.ipynb
basic_feature_extraction.html		basic_feature_extraction.html
basic_feature_extraction.ipynb		basic_feature_extraction.ipynb
basic_mir.html		basic_mir.html
basic_mir.ipynb		basic_mir.ipynb
beat_tracking.html		beat_tracking.html
beat_tracking.ipynb		beat_tracking.ipynb
chroma.html		chroma.html
chroma.ipynb		chroma.ipynb
crossValidationTemplate.py		crossValidationTemplate.py
cross_validation.html		cross_validation.html
cross_validation.ipynb		cross_validation.ipynb
custom.css		custom.css
dp.html		dp.html
dp.ipynb		dp.ipynb
dtw.html		dtw.html
dtw.ipynb		dtw.ipynb
dtw_example.html		dtw_example.html
dtw_example.ipynb		dtw_example.ipynb
energy.html		energy.html
energy.ipynb		energy.ipynb
evaluation.html		evaluation.html
evaluation.ipynb		evaluation.ipynb
evaluation_onset.html		evaluation_onset.html
evaluation_onset.ipynb		evaluation_onset.ipynb
exercise_genre_recognition.html		exercise_genre_recognition.html
exercise_genre_recognition.ipynb		exercise_genre_recognition.ipynb
feature_sonification.html		feature_sonification.html
feature_sonification.ipynb		feature_sonification.ipynb
fourier_transform.html		fourier_transform.html
fourier_transform.ipynb		fourier_transform.ipynb
genre_recognition.html		genre_recognition.html
genre_recognition.ipynb		genre_recognition.ipynb
get_good_at_ipython.html		get_good_at_ipython.html
get_good_at_ipython.ipynb		get_good_at_ipython.ipynb
hpss.html		hpss.html
hpss.ipynb		hpss.ipynb
index.html		index.html
index.ipynb		index.ipynb
intro_mir_eval.html		intro_mir_eval.html
intro_mir_eval.ipynb		intro_mir_eval.ipynb
ipython_audio.html		ipython_audio.html
ipython_audio.ipynb		ipython_audio.ipynb
kmeans.html		kmeans.html
kmeans.ipynb		kmeans.ipynb
kmeans_instrument_classification.html		kmeans_instrument_classification.html
kmeans_instrument_classification.ipynb		kmeans_instrument_classification.ipynb
knn.html		knn.html
knn.ipynb		knn.ipynb
knn_instrument_classification.html		knn_instrument_classification.html
knn_instrument_classification.ipynb		knn_instrument_classification.ipynb
lcs.html		lcs.html
lcs.ipynb		lcs.ipynb
lsh_fingerprinting.html		lsh_fingerprinting.html
lsh_fingerprinting.ipynb		lsh_fingerprinting.ipynb
magnitude_scaling.html		magnitude_scaling.html
magnitude_scaling.ipynb		magnitude_scaling.ipynb
mfcc.html		mfcc.html
mfcc.ipynb		mfcc.ipynb
neural_networks.html		neural_networks.html
neural_networks.ipynb		neural_networks.ipynb
nmf.html		nmf.html
nmf.ipynb		nmf.ipynb
nmf_source_separation.html		nmf_source_separation.html
nmf_source_separation.ipynb		nmf_source_separation.ipynb
novelty_functions.html		novelty_functions.html
novelty_functions.ipynb		novelty_functions.ipynb
numpy_basics.html		numpy_basics.html
numpy_basics.ipynb		numpy_basics.ipynb
onset_detection.html		onset_detection.html
onset_detection.ipynb		onset_detection.ipynb
onset_segmentation.html		onset_segmentation.html
onset_segmentation.ipynb		onset_segmentation.ipynb
pca.html		pca.html
pca.ipynb		pca.ipynb
peak_picking.html		peak_picking.html
peak_picking.ipynb		peak_picking.ipynb
pitch_transcription_exercise.html		pitch_transcription_exercise.html
pitch_transcription_exercise.ipynb		pitch_transcription_exercise.ipynb
python_basics.html		python_basics.html
python_basics.ipynb		python_basics.ipynb

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

musicinformationretrieval.com

How to Use This Repo

Install Python and relevant libraries

Troubleshooting

Start the Jupyter Notebook

Contributions

About

Releases

Packages

Languages

License

meinardmueller/musicinformationretrieval.com

Folders and files

Latest commit

History

Repository files navigation

musicinformationretrieval.com

How to Use This Repo

Install Python and relevant libraries

Troubleshooting

Start the Jupyter Notebook

Contributions

About

Resources

License

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages