simple_src

A simple sample rate conversion lib for audio.

Usage

Usually use sinc Converter, it is flexible and high-quality. The linear Converter is not recommended unless performance is really important and quality is not cared.

sinc

With new method:

use simple_src::{sinc, Convert};

let samples = vec![1.0, 2.0, 3.0, 4.0];
let manager = sinc::Manager::new(2.0, 48.0, 8, 0.1).unwrap();
let mut converter = manager.converter();
for s in converter.process(samples.into_iter()) {
    println!("{s}");
}

Or use builder:

use simple_src::{sinc, Convert};

let samples = vec![1.0, 2.0, 3.0, 4.0];
let manager = sinc::Manager::builder()
    .ratio(2.0)
    .attenuation(48.0)
    .quantify(8)
    .pass_width(0.9)
    .build()
    .unwrap();
let mut converter = manager.converter();
for s in converter.process(samples.into_iter()) {
    println!("{s}");
}

For multi-channel example see two_channels.rs.

linear

use simple_src::{linear, Convert};

let samples = vec![1.0, 2.0, 3.0, 4.0];
let manager = linear::Manager::new(2.0).unwrap();
let mut converter = manager.converter();
for s in converter.process(samples.into_iter()) {
    println!("{s}");
}

Sinc parameters

Recommended initialization parameters for sinc converter:

	attenuation	quantify
8bit fast	48	8
8bit medium	60	16
8bit better	72	32
16bit lower	84	64
16bit fast	96	128
16bit medium	108	256
16bit better	120	512
24bit lower	132	1024
24bit fast	144	2048
24bit medium	156	4096
24bit better	168	8192

The relationship between attenuation and quantify is about Q = 2 ^ (A / 12 - 1), A = 12 + 12 * log2(Q).

Due to the amount of calculation and the size of LUT, A = 144 or 156 for 24bit audio is usually fine, and for 16bit, A = 120 is enough.

Plots

Use plots.py to show the results of conversion. It needs numpy, scipy and matplotlib.

Here is an example showing the results of a downsampling 96kHz:

$ cargo test -r --test testwav -- --ignored --exact --show-output generate
$ cargo test -r --test sinc -- --ignored --exact --show-output ta120_2_96k_down
$ python
>>> import plots
>>> import os
>>> os.chdir('output')
>>> plots.spectrum('beep_96k_44k_s_a120_2.wav')
>>> plots.spectrogram('sweep_96k_44k_s_a120_2.wav')
>>> plots.impulse('impulse_96k_44k_s_a120_2.wav')
>>> plots.impulse('impulse_96k_44k_s_a120_2.wav', True)

See code in tests for more details.

References

1. Smith, J.O. Digital Audio Resampling Home Page https://ccrma.stanford.edu/~jos/resample/.
2. Alan V. Oppenheim, Ronald W. Schafer. Discrete-Time Signal Processing, Third Edition.