Cover Song Recognition Much?

A pile of code for creating a neural network which is capable of identifying cover songs.

I created this project for my own exploratory / learning purposes, but there's been enough requests from others to "see the code" that I've decided to clean things up a bit for the masses.

Instructions for running this whole thing

Note: Mileage may vary. I've run this project on both Mac OS and a couple flavors of Ubuntu. Winoughs might not work. If you run into issues, just open an issue and we'll see if we can figure it out. Second note: I use conda for most of these things as it seems to treat me well.

• Install Python 3.6 using.
• Install the packages from setup.py. Making a new env just for this project isn't a terrible idea.
• Place your training MP3 / WMA / M4A / WAV files in a directory
• Place your validation MP3 / WMA / M4A / WAV files in another directory. The filename must match with the matching song from the training set. The full path isn't used.
• Update root_*_dir properties in core.py to point to your directories.
• Update the tempo_map property in core.py with the relevant BPMs for your songs. Alternatively, if you like to live dangerously, just update the code to allow Librosa to auto-detect the BPM without any hints if no entry is found in tempo_map for a given song.
• Run the 5 functions specified at the bottom of core.py
• Update filenames in train_raw_lstm.py to match any filename changes from core.py.
• Run train_raw_lstm.py. I typically do this by pasting everything in to the Python shell. Because I like interactivity when things go awry.
• Update paths as necessary in testing.py. Pass in full paths to songs you want validated against your new model like so python -m audio_much.testing ~/Desktop/hit_me_with_your_best_shot.mp3

By Stephen Hopper