A machine learning model that classifies what guitar chord is being played and the quality of it: i.e if it's ringy, clear, or muted.
Data not included
- Built in python3.
- Clone this repository.
git clone https://github.com/McCrearyD/Guitar_Chord_Classifier
- To ONLY process raw audio into spectrograms for training, run
python3 -u convert_data.py
inside the main directory. - More instructions will follow as more features come out! Currently under development!
- Record your chord and note whether it's clear, ringy, or muted AND what exact chord you're playing.
- For example, [G, ringy]
- Create a file in the root directory and name it
raw_data
if there isn't already one. - Inside the
raw_data
directory, create a new directory for the chord name (again, if there isn't already one). - Inside
raw_data/${chord_name}
, create yet another directory for the "quality" of the strum, in this case "ringy". - You should have a directory set up similar to:
raw_data/G/ringy
in this example case. - INSIDE the
ringy
sub-directory, append all files that are "ringy Gs". - Repeat this process for any chords, qualities, etc. you wish to train on.
Note: All audio file types are accepted for raw data, as they will be converted into spectrogram images. Also all multi-channel audio files will be converted to 1.
FILE SIZES MUST BE A MINIMUM OF 2 SECONDS. ALL INPUT DATA WILL BE TRUNCATED FROM THE BEGINNING TO 2 SECONDS