Part 8: Next Steps

Hello, it’s Part 8, which is the last part! Hooray! We wanted to recap how this relates to academia, and end by listing some other worthwhile resources, for both writing excellent software and for doing excellent MIR.

How Else Might This Relate to ISMIR & Academia Again?

So in addition to making your time writing software calmer, more relaxing, and more stable, here are some other usecases you can move on to:

• You can tag each version of your paper or thesis in git and on GitHub as it gets submitted, revised, and accepted!
• You can export your entire conda environment so that people can easily reproduce it and easily run your code!
• You can use Zenodo to make DOIs (unique, citiable identifiers) that will make it easier to cite your software!

There’s also an entire other tutorial about handling the data that goes with your software and machine learning software … which we really don’t have space to get into, but the same sorts of principles apply!

Software Engineering Resources

Software Carpentry teaches a huge range of basic software skills, from command lines to version control to R to Python to SQL.

So You Want To Be A Wizard is a fantastic distillation of how to work with computers and how to get better at them, by the amazing Julia Evans.

Learn Python The Hard Way teaches Python starting from 0, and goes up to a fairly advanced level. The books cost money, but has free samples!

This project has a good overview of Python testing practices, and this thread has even more testing resources.

MIR Resources & Tools

musicinformationretrieval.com is a great resource for just about everything about MIR.

Some other packages you might want to look at include crema, pescador, crepe, pysox, msaf, and motif.

Useful datasets and APIs include lakhmidi, the Million Song Dataset, the Million Playlist Dataset, the Spotify API, and the Audio Content Analysis List.

Finally, if you want to learn more about DSP and audio processing, you could take a look at Julius Smith’s books, as well as the teaching parts of Audio Content Analysis.

Success!

We did … just about everything!

We went over installing things in Part 1, and setting up web servives in Part 2. Part 3 covered git and version control, and Part 4 went over Python and Python module structure. Testing was covered in Part 5, and documentation in Part 6. Finally, Part 7 introduced the wider Python ecosystem, and this is Part 8. There’s also the Appendix.

Thanks for coming along for the ride, and please feel free to ask us questions over the rest of the conference!