In music production, there is often a step that can be a bit of a headache, and that’s mastering. It’s the final touch that’s added to a piece of music to enhance certain characteristics. For example, you can adjust certain frequencies, improve stereo aspects, remove certain noise artifacts, etc., to achieve a polished sound that’s optimized for all playback systems, from vinyl to streaming to CDs.
Fortunately, there are tools that can handle this step for you. One such tool is an open-source project called Matchering, coded in Python. Matchering takes your track as input, as well as a reference track that you want your track to match.
The Matchering algorithm allows you to give your music the same sound as your favorite artist’s music. This also enables all the songs on an album to sound harmonious. Matchering analyzes the reference track and extracts the root mean square (RMS), frequency response, amplitude, and stereo spatialization. It then applies these same parameters to your track.
Since it’s a free and open-source software, you can install it via Docker on your own machine. It’s also a Python library, so you can integrate its features into your tools.
import matchering as mg Sending all log messages to the default print function Just delete the following line to work silently mg.log(print) mg.process( # The track you want to master target="my_song.wav", # Some "wet" reference track reference="some_popular_song.wav", # Where and how to save your results results=[ mg.pcm16("my_song_master_16bit.wav"), mg.pcm24("my_song_master_24bit.wav"), ], )
If you’re unable to install Matchering on your machine, but you still want to try it out, there are services like Songmastr and Moises that have implemented it (with some free credits).
Now, if you’re a music producer or sound engineer, it’s worth taking a look at Docker by clicking here.”