A modified version of the bliss-audio to remove ffmpeg and replace it with Rust's symphonia library.

bliss music analyzer - 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 instance, you can use it to make calm playlists to help you sleeping, fast playlists to get you started during the day, etc.

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, take a look at examples/distance.rs and examples/analyze.rs.

If you simply want to try out making playlists from a folder containing songs, this example contains all you need. Usage:

    cargo run --features=serde --release --example=playlist /path/to/folder /path/to/first/song

Don't forget the --release flag!

By default, it outputs the playlist to stdout, but you can use -o <path> to output it to a specific path.

To avoid having to analyze the entire folder several times, it also stores the analysis in /tmp/analysis.json. You can customize this behavior by using -a <path> to store this file in a specific place.

Ready to use code examples:

Compute the distance between two songs

use bliss_audio::{BlissError, Song};

fn main() -> Result<(), BlissError> {
    let song1 = Song::from_path("/path/to/song1")?;
    let song2 = Song::from_path("/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::from_path(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 module. It implements common analysis functions, and allows analyzed songs to be put in a sqlite database seamlessly.

See blissify for a reference implementation.

Cross-compilation

To cross-compile bliss-rs from linux to x86_64 windows, install the x86_64-pc-windows-gnu target via:

    rustup target add x86_64-pc-windows-gnu

Make sure you have x86_64-w64-mingw32-gcc installed on your computer.

Then run:

    cargo build --target x86_64-pc-windows-gnu --release

Will produce a .rlib library file. If you want to generate a shared .dll library, add:

    [lib]
    crate-type = ["cdylib"]

to Cargo.toml before compiling, and if you want to generate a .lib static library, add:

    [lib]
    crate-type = ["staticlib"]

You can of course test the examples yourself by compiling them as .exe:

    cargo build --target x86_64-pc-windows-gnu --release --examples

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.