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README.md |
bliss music analyser - Rust version
bliss-rs is the Rust improvement of bliss, a library used to make playlists by analyzing songs, and computing distance between them.
Like bliss, it eases the creation of « intelligent » playlists and/or continuous play, à la Spotify/Grooveshark Radio, as well as easing creating plug-ins for existing audio players.
For now (and if you're looking for an easy-to use smooth play experience), blissify implements bliss for MPD.
There are also python bindings.
Note 1: the features bliss-rs outputs is not compatible with the ones used by C-bliss, since it uses different, more accurate values, based on actual literature. It is also faster.
Note 2: The bliss-rs
crate is outdated. You should use bliss-audio
(this crate) instead.
Examples
For simple analysis / distance computing, a look at examples/distance.rs
and
examples/analyse.rs
.
Ready to use examples:
Compute the distance between two songs
use bliss_audio::Song;
fn main() {
let song1 = Song::new("/path/to/song1");
let song2 = Song::new("/path/to/song2");
println!("Distance between song1 and song2 is {}", song1.distance(song2));
}
Make a playlist from a song
use bliss_rs::{BlissError, Song};
use ndarray::{arr1, Array};
use noisy_float::prelude::n32;
fn main() -> Result<(), BlissError> {
let paths = vec!["/path/to/song1", "/path/to/song2", "/path/to/song3"];
let mut songs: Vec<Song> = paths
.iter()
.map(|path| Song::new(path))
.collect::<Result<Vec<Song>, BlissError>>()?;
// Assuming there is a first song
let analysis_first_song = arr1(&songs[0].analysis);
// Identity matrix used to compute the distance.
// Note that it can be changed to alter feature ponderation, which
// may yield to better playlists (subjectively).
let m = Array::eye(analysis_first_song.len());
songs.sort_by_cached_key(|song| {
n32((arr1(&song.analysis) - &analysis_first_song)
.dot(&m)
.dot(&(arr1(&song.analysis) - &analysis_first_song)))
});
println!(
"Playlist is: {:?}",
songs
.iter()
.map(|song| &song.path)
.collect::<Vec<&String>>()
);
Ok(())
}
Further use
Instead of reinventing ways to fetch a user library, play songs, etc, and embed that into bliss, it is easier to look at the Library trait.
By implementing a few functions to get songs from a media library, and store the resulting analysis, you get access to functions to analyze an entire library (with multithreading), and to make playlists easily.
See blissify for a reference implementation.
Acknowledgements
- This library relies heavily on aubio's Rust bindings for the spectral / timbral analysis, so a big thanks to both the creators and contributors of librosa, and to @katyo for making aubio bindings for Rust.
- The first part of the chroma extraction is basically a rewrite of librosa's chroma feature extraction from python to Rust, with just as little features as needed. Thanks to both creators and contributors as well.
- Finally, a big thanks to Christof Weiss for pointing me in the right direction for the chroma feature summarization, which are basically also a rewrite from Python to Rust of some of the awesome notebooks by AudioLabs Erlangen, that you can find here.