madmom VS inaSpeechSegmenter

There are also online & offline onset detection approaches available as part of the Python madmom library https://github.com/CPJKU/madmom as binaries and Python classes. The methods included in madmom have shown state of the art results in multiple Music Information Retrieval Evaluation eXchange (MIREX) campaigns in recent years. Hope that's useful to you.

In BoxVR's case, they literally use a machine learning model. It's cutting edge tech. Read more about it here: The paper, the code (and a sub-paper)

I have a little hobby project where I record an FM radio music station using a SDR and then remove all the non-music portions for offline listening. I like the music selections the DJs pick, but I prefer not to listen to the DJ commentary and the advertisements.

I evaluated three methods of recording: analog capture from a standalone FM receiver, using this nrsc5 library to record the "HD" radio stream, and using an AirSpy SDR with this library: https://github.com/jj1bdx/airspy-fmradion

Recording the "HD" (what a misnomer) radio was nice in that there was no hiss or multipath effects, but in comparison to the other methods the digital compression artifacts became impossible to un-hear. It seems to top out at about 96 kbps

The airspy-fmradion library has some nice stuff in it to address multipath, resulting in the best audio quality of the three methods I tested.

I use https://github.com/ina-foss/inaSpeechSegmenter to identify which segments of the recordings are speech vs. music.

One key library that makes it possible is this one: https://github.com/ina-foss/inaSpeechSegmenter

After looking for a few options, I came across inaSpeechSegmenter. It is a speech segmenter and if you pass it an audio file, it returns the time segments of noises and music.