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.

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::{BlissError, Song};

fn main() -> Result<(), BlissError> {
    let song1 = Song::new("/path/to/song1")?;
    let song2 = Song::new("/path/to/song2")?;
        
    println!("Distance between song1 and song2 is {}", song1.distance(&song2));
    Ok(())
}

Make a playlist from a song

use bliss_audio::{BlissError, Song};
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 first_song = songs.first().unwrap().to_owned();

    songs.sort_by_cached_key(|song| n32(first_song.distance(&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.

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