102 lines
4.0 KiB
Markdown
102 lines
4.0 KiB
Markdown
[](https://crates.io/crates/bliss-audio)
|
|
[](https://github.com/Polochon-street/bliss-rs/actions)
|
|
[](https://docs.rs/bliss-audio/)
|
|
|
|
# bliss music analyser - Rust version
|
|
bliss-rs is the Rust improvement of [bliss](https://github.com/Polochon-street/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](https://crates.io/crates/blissify) implements bliss for
|
|
[MPD](https://www.musicpd.org/).
|
|
|
|
There are also [python](https://pypi.org/project/bliss-audio/) 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](https://lelele.io/thesis.pdf). 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](https://github.com/Polochon-street/bliss-rs/blob/master/src/library.rs#L12)
|
|
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](https://crates.io/crates/blissify) for a reference
|
|
implementation.
|
|
|
|
## Acknowledgements
|
|
|
|
* This library relies heavily on [aubio](https://aubio.org/)'s
|
|
[Rust bindings](https://crates.io/crates/aubio-rs) 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](https://librosa.org/doc/latest/index.html)'s
|
|
[chroma feature extraction](https://librosa.org/doc/latest/generated/librosa.feature.chroma_stft.html?highlight=chroma#librosa.feature.chroma_stftfrom)
|
|
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](https://www.audiolabs-erlangen.de/fau/assistant/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](https://www.audiolabs-erlangen.de/resources/MIR/FMP/C0/C0.html).
|