This is a mixer for "Don't Stop the Music" that uses the results of bliss analysis to find suitable tracks. For details about bliss itself please refer to its website.
There are two parts to this mixer:
The LMS plugin can be installed from my repo
Binaries for the analyser will be placed on the Github releases page. This analyser requires ffmpeg to be installed for Linux and macOS (homebrew), but libraries are bundled with the Windows version. Contained within each ZIP is a README.md file with detailed usage steps. The current ZIPs can be downloaded from:
As a quick guide:
On a 2015-era i7 8 core laptop with SSD I can analyse almost 14000 tracks/hour. Obviously this will vary depending upon track lengths, etc, but gives a rough idea of how long the analysis stage will take.
The analyser only stores relative paths in its database - hence you can analyse on one machine and run the mixer on another. e.g. If you music is stored in /home/user/Music, then /home/user/Music/Artist/Album/01-Track.mp3 is stored in the database as Artist/Album/01-Track.mp3
This mixer and analyser are Rust ports of the Bliss part of MusicSimilarity. I started that plugin to see if merging Essentia with Musly results would improve things, then discovered Bliss. For my music collection Bliss appears to create better mixes, and is much faster than Essentia. Hence this plugin.
to-bliss.py can be used to convert a MusicSimilarity DB file (if it has bliss analysis) into a bliss.db - saving the need to re-analyse music if it has already been analysed with bliss.
There are two parts to this mixer:
- A Linux/macOS/Windows app to analyse your music, save results to an SQLite database, and upload results to LMS
- An LMS plugin that contains pre-built mixer binaries for Linux (x86_64, arm, 64-bit arm), macOS (fat binary), and Windows
The LMS plugin can be installed from my repo
Binaries for the analyser will be placed on the Github releases page. This analyser requires ffmpeg to be installed for Linux and macOS (homebrew), but libraries are bundled with the Windows version. Contained within each ZIP is a README.md file with detailed usage steps. The current ZIPs can be downloaded from:
As a quick guide:
- Install the LMS plugin
- Download the relevant ZIP of bliss-analyser
- Install ffmpeg for Linux or macOS
- Edit 'config.ini' in the bliss-analyser folder to contain the correct path to your music files, and the correct LMS hostname or IP address
- Analyse your files with: bliss-analyser analyse
- Once analysed, upload DB to LMS with: bliss-analyser upload
- Choose 'Bliss' as DSTM mixer in LMS
On a 2015-era i7 8 core laptop with SSD I can analyse almost 14000 tracks/hour. Obviously this will vary depending upon track lengths, etc, but gives a rough idea of how long the analysis stage will take.
The analyser only stores relative paths in its database - hence you can analyse on one machine and run the mixer on another. e.g. If you music is stored in /home/user/Music, then /home/user/Music/Artist/Album/01-Track.mp3 is stored in the database as Artist/Album/01-Track.mp3
This mixer and analyser are Rust ports of the Bliss part of MusicSimilarity. I started that plugin to see if merging Essentia with Musly results would improve things, then discovered Bliss. For my music collection Bliss appears to create better mixes, and is much faster than Essentia. Hence this plugin.
to-bliss.py can be used to convert a MusicSimilarity DB file (if it has bliss analysis) into a bliss.db - saving the need to re-analyse music if it has already been analysed with bliss.
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