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Rename the Soft Vacuum package

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Robbert van der Helm
2023-04-05 20:07:13 +02:00
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# Changelog
All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic
Versioning](https://semver.org/spec/v2.0.0.html).
## [Unreleased]

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might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

16
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[package]
name = "soft_vacuum"
version = "0.1.0"
edition = "2021"
authors = ["Robbert van der Helm <mail@robbertvanderhelm.nl>"]
license = "GPL-3.0-or-later"
homepage = "https://github.com/robbert-vdh/nih-plug/tree/master/plugins/soft_vacuum"
[lib]
crate-type = ["cdylib"]
[dependencies]
nih_plug = { path = "../../", features = ["assert_process_allocs"] }
[dev-dependencies]
approx = "0.5.1"

28
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# Soft Vacuum (Airwindows port)
This is a straightforward port of Airwindows' [Hard
Vacuum](https://www.airwindows.com/hard-vacuum-vst/) plugin with 4x linear-phase
oversampling and parameter smoothing, because I like the distortion effect and
wish it had oversampling. All credit goes to Chris from Airwindows. I just
wanted to share this in case anyone else finds it useful.
## Download
You can download the development binaries for Linux, Windows and macOS from the
[automated
builds](https://github.com/robbert-vdh/nih-plug/actions/workflows/build.yml?query=branch%3Amaster)
page. Or if you're not signed in on GitHub, then you can also find the latest nightly
build [here](https://nightly.link/robbert-vdh/nih-plug/workflows/build/master).
On macOS you may need to [disable
Gatekeeper](https://disable-gatekeeper.github.io/) as Apple has recently made it
more difficult to run unsigned code on macOS.
### Building
After installing [Rust](https://rustup.rs/), you can compile Safety Limiter as
follows:
```shell
cargo xtask bundle soft_vacuum --release
```

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// Soft Vacuum: Airwindows Hard Vacuum port with oversampling
// Copyright (C) 2023 Robbert van der Helm
// Copyright (c) 2018 Chris Johnson
//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <https://www.gnu.org/licenses/>.
use std::f32::consts::{FRAC_PI_2, PI};
use nih_plug::nih_debug_assert;
/// For some reason this constant is used quite a few times in the Hard Vacuum implementation. I'm
/// pretty sure it's a typo.
const ALMOST_FRAC_PI_2: f32 = 1.557_079_7;
/// Single-channel port of the Hard Vacuum algorithm from
/// <https://github.com/airwindows/airwindows/blob/283343b9e90c28fdb583f27e198f882f268b051b/plugins/LinuxVST/src/HardVacuum/HardVacuumProc.cpp>.
#[derive(Debug, Default)]
pub struct HardVacuum {
last_sample: f32,
}
/// Parameters for the [`HardVacuum`] algorithm. This is a struct to make it easier to reuse the
/// same values for multiple channels.
pub struct Params {
/// The 'drive' parameter, should be in the range `[0, 2]`. Controls both the drive and how many
/// distortion stages are applied.
pub drive: f32,
/// The 'warmth' parameter, should be in the range `[0, 1]`.
pub warmth: f32,
/// The 'aura' parameter, should be in the range `[0, pi]`.
pub aura: f32,
}
impl HardVacuum {
/// Reset the processor's state. In this case this only resets the discrete derivative
/// calculation. Doesn't make a huge difference but it's still useful to make the effect
/// deterministic.
pub fn reset(&mut self) {
self.last_sample = 0.0;
}
/// Process a sample for a single channel. Because this maintains per-channel internal state,
/// you should use different [`HardVacuum`] objects for each channel when processing
/// multichannel audio.
///
/// Output scaling and dry/wet mixing should be done externally.
#[allow(unused)]
pub fn process(&mut self, input: f32, params: &Params) -> f32 {
let slew = self.compute_slew(input);
self.process_with_slew(input, params, slew)
}
/// Compute only the slew value. Used together with `process_with_slew()` to compute the slews,
/// upsample that, and then process the upsampled signal using those upsampled slews so the
/// oversampled version ends up sounding more similar to the original algorithm.
pub fn compute_slew(&mut self, input: f32) -> f32 {
// AW: skew will be direction/angle
let skew = input - self.last_sample;
self.last_sample = input;
skew
}
/// The same as `process()`, but with an externally computed slew value (`input - last_value`).
/// This is useful for the oversampled version of this algorithm as we can upsample the slew
/// signal separately.
pub fn process_with_slew(&self, input: f32, params: &Params, slew: f32) -> f32 {
// We'll skip a couple unnecessary things here like the dithering and the manual denormal
// evasion
nih_debug_assert!((0.0..=2.0).contains(&params.drive));
nih_debug_assert!((0.0..=1.0).contains(&params.warmth));
nih_debug_assert!((0.0..=PI).contains(&params.aura));
// These two values are derived from the warmth parameter in an ...interesting way
let scaled_warmth = params.warmth / FRAC_PI_2;
let inverse_warmth = 1.0 - params.warmth;
// AW: We're doing all this here so skew isn't incremented by each stage
let skew = {
// AW: skew will be direction/angle
let skew = slew;
// AW: for skew we want it to go to zero effect again, so we use full range of the sine
let bridge_rectifier = skew.abs().min(PI).sin();
// AW: skew is now sined and clamped and then re-amplified again
// AW @ the `* 1.557` part: cools off sparkliness and crossover distortion
// NOTE: The 1.55707 is presumably a typo in the original plugin. `pi/2` is 1.5707...,
// and this one has an additional 5 in there.
skew.signum() * bridge_rectifier * params.aura * input * ALMOST_FRAC_PI_2
};
// AW: WE MAKE LOUD NOISE! RAWWWK!
let mut remaining_distortion_stages = if params.drive > 1.0 {
params.drive * params.drive
} else {
params.drive
};
// AW: crank up the gain on this so we can make it sing
let mut output = input;
while remaining_distortion_stages > 0.0 {
// AW: full crank stages followed by the proportional one whee. 1 at full warmth to
// 1.5570etc at no warmth
let drive = if remaining_distortion_stages > 1.0 {
ALMOST_FRAC_PI_2
} else {
remaining_distortion_stages * (1.0 + ((ALMOST_FRAC_PI_2 - 1.0) * inverse_warmth))
};
// AW: set up things so we can do repeated iterations, assuming that wet is always going
// to be 0-1 as in the previous plug.
let bridge_rectifier = (output.abs() + skew).min(FRAC_PI_2).sin();
// AW: the distortion section.
let bridge_rectifier = bridge_rectifier.mul_add(drive, skew).min(FRAC_PI_2).sin();
output = if output > 0.0 {
let positive = drive - scaled_warmth;
(output * (1.0 - positive + skew)) + (bridge_rectifier * (positive + skew))
} else {
let negative = drive + scaled_warmth;
(output * (1.0 - negative + skew)) - (bridge_rectifier * (negative + skew))
};
remaining_distortion_stages -= 1.0;
}
output
}
}

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// Soft Vacuum: Airwindows Hard Vacuum port with oversampling
// Copyright (C) 2023 Robbert van der Helm
//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <https://www.gnu.org/licenses/>.
use std::f32::consts::PI;
use std::sync::atomic::Ordering;
use std::sync::Arc;
use nih_plug::prelude::*;
mod hard_vacuum;
mod oversampling;
/// The maximum number of samples to process at a time. Used to create scratch buffers for the
/// oversampling.
const MAX_BLOCK_SIZE: usize = 32;
/// The 2-logarithm of the maximum oversampling amount to use. 16x oversampling corresponds to factor
/// 4.
const MAX_OVERSAMPLING_FACTOR: usize = 4;
const MAX_OVERSAMPLING_TIMES: usize = oversampling_factor_to_times(MAX_OVERSAMPLING_FACTOR);
const MAX_OVERSAMPLED_BLOCK_SIZE: usize = MAX_BLOCK_SIZE * MAX_OVERSAMPLING_TIMES;
/// This corresponds to 2x oversampling.
const DEFAULT_OVERSAMPLING_FACTOR: usize = 1;
struct SoftVacuum {
params: Arc<SoftVacuumParams>,
/// Stores implementations of the Hard Vacuum algorithm for each channel, since each channel
/// needs to maintain its own state.
hard_vacuum_processors: Vec<hard_vacuum::HardVacuum>,
/// Oversampling for each channel.
oversamplers: Vec<oversampling::Lanczos3Oversampler>,
/// Oversampling for each channel's slew control signal. This is upsampled separately to make
/// the oversampled algorithm sound similar to the regular, non oversampled version as the slews
/// will necessarily be lower in the oversampled version.
slew_oversamplers: Vec<oversampling::Lanczos3Oversampler>,
/// Scratch buffers that the smoothed parameters can be rendered to. Allocated on the heap
/// because Windows uses tiny stack sizes which may eventually cause problems in some hosts.
scratch_buffers: Box<ScratchBuffers>,
}
struct ScratchBuffers {
// These are for the Hard Vacuum parameters
drive: [f32; MAX_OVERSAMPLED_BLOCK_SIZE],
warmth: [f32; MAX_OVERSAMPLED_BLOCK_SIZE],
aura: [f32; MAX_OVERSAMPLED_BLOCK_SIZE],
// These are for the mix parameters
output_gain: [f32; MAX_OVERSAMPLED_BLOCK_SIZE],
dry_wet_ratio: [f32; MAX_OVERSAMPLED_BLOCK_SIZE],
}
impl Default for ScratchBuffers {
fn default() -> Self {
Self {
drive: [0.0; MAX_OVERSAMPLED_BLOCK_SIZE],
warmth: [0.0; MAX_OVERSAMPLED_BLOCK_SIZE],
aura: [0.0; MAX_OVERSAMPLED_BLOCK_SIZE],
output_gain: [0.0; MAX_OVERSAMPLED_BLOCK_SIZE],
dry_wet_ratio: [0.0; MAX_OVERSAMPLED_BLOCK_SIZE],
}
}
}
// The parameters are the same as in the original plugin, except that they have different value
// names
#[derive(Params)]
struct SoftVacuumParams {
/// The drive/multistage parameter. Goes from `[0, 2]`, which is displayed as `0%` through
/// `200%`. Above 100% up to four distortion stages are applied.
#[id = "drive"]
drive: FloatParam,
/// The 'warmth' DC bias parameter. Shown as a percentage in this version.
#[id = "warmth"]
warmth: FloatParam,
/// The 'aura' parameter which is essentially extra input gain. Shown as a percentage, but maps
/// to a `[0, pi]` value.
#[id = "aura"]
aura: FloatParam,
/// The output gain, shown in decibel.
#[id = "output_gain"]
pub output_gain: FloatParam,
/// A linear dry/wet mix parameter.
#[id = "dry_wet_ratio"]
pub dry_wet_ratio: FloatParam,
/// The current oversampling factor. This is the 2-logarithm of the oversampling amount. 0
/// corresponds to 1x/no oversampling, 1 to 2x oversampling, 2 to 4x, etc..
#[id = "oversampling_factor"]
pub oversampling_factor: IntParam,
}
impl Default for SoftVacuumParams {
fn default() -> Self {
// This is set by the `oversampling_factor` parameter and is used by the smoothers of the
// other parmaeters so the oversampling amount always stays in sync
let oversampling_times = Arc::new(AtomicF32::new(oversampling_factor_to_times(
DEFAULT_OVERSAMPLING_FACTOR,
) as f32));
Self {
// Goes up to 200%, with the second half being nonlinear
drive: FloatParam::new("Drive", 0.0, FloatRange::Linear { min: 0.0, max: 2.0 })
.with_unit("%")
.with_smoother(SmoothingStyle::OversamplingAware(
oversampling_times.clone(),
&SmoothingStyle::Linear(20.0),
))
.with_value_to_string(formatters::v2s_f32_percentage(0))
.with_string_to_value(formatters::s2v_f32_percentage()),
warmth: FloatParam::new("Warmth", 0.0, FloatRange::Linear { min: 0.0, max: 1.0 })
.with_unit("%")
.with_smoother(SmoothingStyle::OversamplingAware(
oversampling_times.clone(),
&SmoothingStyle::Linear(10.0),
))
.with_value_to_string(formatters::v2s_f32_percentage(0))
.with_string_to_value(formatters::s2v_f32_percentage()),
aura: FloatParam::new("Aura", 0.0, FloatRange::Linear { min: 0.0, max: PI })
.with_unit("%")
.with_smoother(SmoothingStyle::OversamplingAware(
oversampling_times.clone(),
&SmoothingStyle::Linear(10.0),
))
// We're displaying the value as a percentage even though it goes from `[0, pi]`
.with_value_to_string({
let formatter = formatters::v2s_f32_percentage(0);
Arc::new(move |value| formatter(value / PI))
})
.with_string_to_value({
let formatter = formatters::s2v_f32_percentage();
Arc::new(move |string| formatter(string).map(|value| value * PI))
}),
output_gain: FloatParam::new(
"Output Gain",
util::db_to_gain(0.0),
FloatRange::Skewed {
min: util::db_to_gain(-40.0),
max: util::db_to_gain(0.0),
factor: FloatRange::gain_skew_factor(-40.0, 0.0),
},
)
.with_unit(" dB")
// The value does not go down to 0 so we can do logarithmic here
.with_smoother(SmoothingStyle::OversamplingAware(
oversampling_times.clone(),
&SmoothingStyle::Logarithmic(10.0),
))
.with_value_to_string(formatters::v2s_f32_gain_to_db(2))
.with_string_to_value(formatters::s2v_f32_gain_to_db()),
dry_wet_ratio: FloatParam::new("Mix", 1.0, FloatRange::Linear { min: 0.0, max: 1.0 })
.with_unit("%")
.with_smoother(SmoothingStyle::OversamplingAware(
oversampling_times.clone(),
&SmoothingStyle::Linear(10.0),
))
.with_value_to_string(formatters::v2s_f32_percentage(0))
.with_string_to_value(formatters::s2v_f32_percentage()),
oversampling_factor: IntParam::new(
"Oversampling",
DEFAULT_OVERSAMPLING_FACTOR as i32,
IntRange::Linear {
min: 0,
max: MAX_OVERSAMPLING_FACTOR as i32,
},
)
.with_unit("x")
.with_callback(Arc::new(move |new_factor| {
oversampling_times.store(
oversampling_factor_to_times(new_factor as usize) as f32,
Ordering::Relaxed,
);
}))
.with_value_to_string(Arc::new(|value| {
// NIH-plug prevents `value` from being out of range and thus negative
let oversampling_times = oversampling_factor_to_times(value as usize);
oversampling_times.to_string()
}))
.with_string_to_value(Arc::new(|string| {
let oversampling_times: usize = string.parse().ok()?;
Some(oversampling_times_to_factor(oversampling_times) as i32)
})),
}
}
}
impl Default for SoftVacuum {
fn default() -> Self {
Self {
params: Arc::new(SoftVacuumParams::default()),
hard_vacuum_processors: Vec::new(),
oversamplers: Vec::new(),
slew_oversamplers: Vec::new(),
scratch_buffers: Box::default(),
}
}
}
impl Plugin for SoftVacuum {
const NAME: &'static str = "Soft Vacuum";
const VENDOR: &'static str = "Robbert van der Helm";
const URL: &'static str = env!("CARGO_PKG_HOMEPAGE");
const EMAIL: &'static str = "mail@robbertvanderhelm.nl";
const VERSION: &'static str = env!("CARGO_PKG_VERSION");
const AUDIO_IO_LAYOUTS: &'static [AudioIOLayout] = &[
AudioIOLayout {
main_input_channels: NonZeroU32::new(2),
main_output_channels: NonZeroU32::new(2),
..AudioIOLayout::const_default()
},
AudioIOLayout {
main_input_channels: NonZeroU32::new(1),
main_output_channels: NonZeroU32::new(1),
..AudioIOLayout::const_default()
},
];
type SysExMessage = ();
type BackgroundTask = ();
fn params(&self) -> Arc<dyn Params> {
self.params.clone()
}
fn initialize(
&mut self,
audio_io_layout: &AudioIOLayout,
_buffer_config: &BufferConfig,
context: &mut impl InitContext<Self>,
) -> bool {
let num_channels = audio_io_layout
.main_output_channels
.expect("Plugin was initialized without any outputs")
.get() as usize;
self.hard_vacuum_processors
.resize_with(num_channels, hard_vacuum::HardVacuum::default);
self.oversamplers.resize_with(num_channels, || {
oversampling::Lanczos3Oversampler::new(MAX_BLOCK_SIZE, MAX_OVERSAMPLING_FACTOR)
});
self.slew_oversamplers.resize_with(num_channels, || {
oversampling::Lanczos3Oversampler::new(MAX_BLOCK_SIZE, MAX_OVERSAMPLING_FACTOR)
});
if let Some(oversampler) = self.oversamplers.first() {
context.set_latency_samples(
oversampler.latency(self.params.oversampling_factor.value() as usize),
);
}
true
}
fn reset(&mut self) {
for hard_vacuum in &mut self.hard_vacuum_processors {
hard_vacuum.reset();
}
for oversampler in &mut self.oversamplers {
oversampler.reset();
}
for oversampler in &mut self.slew_oversamplers {
oversampler.reset();
}
}
fn process(
&mut self,
buffer: &mut Buffer,
_aux: &mut AuxiliaryBuffers,
context: &mut impl ProcessContext<Self>,
) -> ProcessStatus {
let oversampling_factor = self.params.oversampling_factor.value() as usize;
let oversampling_times = oversampling_factor_to_times(oversampling_factor);
// If the oversampling factor parameter is changed then the host needs to know about the new
// latency
if let Some(oversampler) = self.oversamplers.first() {
context.set_latency_samples(oversampler.latency(oversampling_factor));
}
for (_, block) in buffer.iter_blocks(MAX_BLOCK_SIZE) {
let block_len = block.samples();
let upsampled_block_len = block_len * oversampling_times;
// These are the parameters for the distortion algorithm
let drive = &mut self.scratch_buffers.drive;
self.params
.drive
.smoothed
.next_block(drive, upsampled_block_len);
let warmth = &mut self.scratch_buffers.warmth;
self.params
.warmth
.smoothed
.next_block(warmth, upsampled_block_len);
let aura = &mut self.scratch_buffers.aura;
self.params
.aura
.smoothed
.next_block(aura, upsampled_block_len);
// And the general output mixing
let output_gain = &mut self.scratch_buffers.output_gain;
self.params
.output_gain
.smoothed
.next_block(output_gain, upsampled_block_len);
let dry_wet_ratio = &mut self.scratch_buffers.dry_wet_ratio;
self.params
.dry_wet_ratio
.smoothed
.next_block(dry_wet_ratio, upsampled_block_len);
for (block_channel, ((oversampler, slew_oversampler), hard_vacuum)) in
block.into_iter().zip(
self.oversamplers
.iter_mut()
.zip(self.slew_oversamplers.iter_mut())
.zip(self.hard_vacuum_processors.iter_mut()),
)
{
// The slew signal is computed and oversampled first. This is then used as a control
// signal in the oversampled version of the algorithm so it sounds more similar to
// the non-oversampled version. Otherwise the slews are necessarily going to be much
// lower.
let mut slews = [0.0f32; MAX_BLOCK_SIZE];
for (sample, slew) in block_channel.iter().zip(slews.iter_mut()) {
*slew = hard_vacuum.compute_slew(*sample);
}
let upsampled_slews =
slew_oversampler.upsample_only(&mut slews, oversampling_factor);
oversampler.process(block_channel, oversampling_factor, |upsampled| {
assert!(upsampled.len() == upsampled_block_len);
for (sample_idx, (sample, slew)) in
upsampled.iter_mut().zip(upsampled_slews).enumerate()
{
// SAFETY: We already made sure that the blocks are equal in size. We could
// zip iterators instead but with six iterators that's already a bit
// too much without a first class way to zip more than two iterators
// together into a single tuple of iterators.
let hard_vacuum_params = hard_vacuum::Params {
drive: unsafe { *drive.get_unchecked(sample_idx) },
warmth: unsafe { *warmth.get_unchecked(sample_idx) },
aura: unsafe { *aura.get_unchecked(sample_idx) },
};
let output_gain = unsafe { *output_gain.get_unchecked(sample_idx) };
let dry_wet_ratio = unsafe { *dry_wet_ratio.get_unchecked(sample_idx) };
let distorted =
hard_vacuum.process_with_slew(*sample, &hard_vacuum_params, *slew);
*sample = (distorted * output_gain * dry_wet_ratio)
+ (*sample * (1.0 - dry_wet_ratio));
}
});
}
}
ProcessStatus::Normal
}
}
// Used in the conversion for the oversampling amount parameter
const fn oversampling_factor_to_times(factor: usize) -> usize {
2usize.pow(factor as u32)
}
const fn oversampling_times_to_factor(times: usize) -> usize {
times.ilog2() as usize
}
impl ClapPlugin for SoftVacuum {
const CLAP_ID: &'static str = "nl.robbertvanderhelm.soft-vacuum";
const CLAP_DESCRIPTION: Option<&'static str> =
Some("Airwindows Hard Vacuum port with oversampling");
const CLAP_MANUAL_URL: Option<&'static str> = Some(Self::URL);
const CLAP_SUPPORT_URL: Option<&'static str> = None;
const CLAP_FEATURES: &'static [ClapFeature] = &[
ClapFeature::AudioEffect,
ClapFeature::Stereo,
ClapFeature::Mono,
ClapFeature::Distortion,
];
}
impl Vst3Plugin for SoftVacuum {
const VST3_CLASS_ID: [u8; 16] = *b"SoftVacuum.RvdH.";
const VST3_SUBCATEGORIES: &'static [Vst3SubCategory] =
&[Vst3SubCategory::Fx, Vst3SubCategory::Distortion];
}
nih_export_clap!(SoftVacuum);
nih_export_vst3!(SoftVacuum);

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// Soft Vacuum: Airwindows Hard Vacuum port with oversampling
// Copyright (C) 2023 Robbert van der Helm
//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <https://www.gnu.org/licenses/>.
use nih_plug::debug::*;
/// The kernel used in `Lanczos3Oversampler`. Specified here as a constant since it is a constant.
/// Precomputed since compile-time floating point arithmetic is still unstable.
///
/// Computed using:
///
/// ```python
/// LANCZOS_A = 3
///
/// x = np.arange(-LANCZOS_A * 2 + 1, LANCZOS_A * 2) / 2
/// np.sinc(x) * np.sinc(x / LANCZOS_A)
/// ```
///
/// Note the `+1` at the start of the range and the lack of `+1` at the (exclusive) end of the
/// range. This is because we can ommit the first and last point because they are always zero.
const LANCZOS3_UPSAMPLING_KERNEL: [f32; 11] = [
0.02431708,
-0.0,
-0.13509491,
0.0,
0.6079271,
1.0,
0.6079271,
0.0,
-0.13509491,
-0.0,
0.02431708,
];
/// `LANCZOS3_UPSAMPLING_KERNEL` divided by two, used for downsampling so that upsampling followed
/// by downsampling results in unity gain.
const LANCZOS3_DOWNSAMPLING_KERNEL: [f32; 11] = [
0.01215854,
-0.0,
-0.06754746,
0.0,
0.30396355,
0.5,
0.30396355,
0.0,
-0.06754746,
-0.0,
0.01215854,
];
/// The latency introduced by the two filter kernels defined above, in samples.
const LANZCOS3_KERNEL_LATENCY: usize = LANCZOS3_UPSAMPLING_KERNEL.len() / 2;
/// A barebones multi-stage linear-phase oversampler that uses the lanzcos kernel with a=3 for a
/// good approximation of a windowed sinc with only a 11 point kernel function (the kernel is
/// actually 13 points, but the outer two points are both zero can can thus be omitted). This can be
/// done much more efficiently but I was in a hurry and this is simple to implement without having
/// to look anything up.
///
/// This only handles a single audio channel. Use multiple instances for multichannel audio.
#[derive(Debug)]
pub struct Lanczos3Oversampler {
/// The state used for each oversampling stage. Also contains stages that are not being used, so
/// the number of stages can change without allocating. The number of currently active
/// stages/the oversampling factor passed to [`process()`][Self::process()] determines how many
/// of these are actually used.
stages: Vec<Lanzcos3Stage>,
/// The oversampler's latency. Precomputed for each possible number of active stages.
latencies: Vec<u32>,
}
/// A single oversampling stage. Contains the ring buffers and current position in that ringbuffer
/// used for convolving the filter with the inputs in the upsampling and downsampling parts of the
/// stage.
#[derive(Debug, Clone)]
struct Lanzcos3Stage {
/// The amount of oversampling that happens at this stage. Will be 2 for the first stage, 4 for
/// the second stage, 8 for the third stage, and so forth. Used to calculate the stage's effect
/// on the oversampling's latency.
oversampling_amount: usize,
/// These ring buffers contain `LANCZOS3_UPSAMPLING_KERNEL.len()` samples. The upsampling ring
/// buffer contains room to delay the signal further to make sure the _total_
/// (upsampling+downsampling) latency imposed on the signal is divisible by the stage's
/// oversampling amount. That is needed to avoid fractional latency.
upsampling_rb: Vec<f32>,
upsampling_write_pos: usize,
/// The additional delay for the upsampling needed to make this stage impose an integer amount
/// of latency. The stage's _total_ (upsampling+downsampling) latency needs to be divisible by
/// the stage's oversampling amount.
additional_upsampling_latency: usize,
/// No additional latency needs to be imposed for the downsampling, so to keep things simple
/// this doesn't add any additional delay.
downsampling_rb: [f32; LANCZOS3_DOWNSAMPLING_KERNEL.len()],
downsampling_write_pos: usize,
scratch_buffer: Vec<f32>,
}
impl Lanczos3Oversampler {
/// Create a new oversampler that can oversample to up to the specified oversampling factor, or
/// the 2-logarithm of the oversampling amount. 1x oversampling (aka, do nothing) = 0, 2x
/// oversampling = 1, 4x oversampling = 3, etc. The actual amount of oversampling stages used is
/// passed to the `process()` function, and must be set to `max_factor` or lower.
pub fn new(maximum_block_size: usize, max_factor: usize) -> Self {
let mut stages = Vec::with_capacity(max_factor);
for stage in 0..max_factor {
stages.push(Lanzcos3Stage::new(maximum_block_size, stage))
}
// Since the number of active oversampling stages is passed to the process function, we also
// need to know the effective latencies of all possible oversampling settings in advance.
let latencies = stages
.iter()
.map(|stage| stage.effective_latency())
.scan(0, |total_latency, latency| {
*total_latency += latency;
Some(*total_latency)
})
.collect();
Self { stages, latencies }
}
/// Reset the oversampling filters to their initial states.
pub fn reset(&mut self) {
for stage in &mut self.stages {
stage.reset();
}
}
/// Get the latency in samples for the given oversampling factor. Fractional latency is
/// automatically avoided.
///
/// # Panics
///
/// Panics if `factor > max_factor`.
pub fn latency(&self, factor: usize) -> u32 {
if factor == 0 {
0
} else {
self.latencies[factor - 1]
}
}
/// Upsample `block` using the specified oversampling factor, process the upsampled version
/// using `f`, and then downsample it again and write the results back to `block` with a
/// [`latency()`][Self::latency()] sample delay.
///
/// # Panics
///
/// Panics if `factor > max_factor`, or if `block`'s length is longer than the maximum block
/// size.
pub fn process(&mut self, block: &mut [f32], factor: usize, f: impl FnOnce(&mut [f32])) {
assert!(factor <= self.stages.len());
// This is the 1x oversampling case, this should also modify the block to be consistent
if factor == 0 {
f(block);
return;
}
assert!(
block.len() <= self.stages[0].scratch_buffer.len() / 2,
"The block's size exceeds the maximum block size"
);
let upsampled = self.upsample_from(block, factor);
f(upsampled);
self.downsample_to(block, factor)
}
/// An upsample-only version of `process` that returns the upsampled version of the signal that
/// would normally be passed to `process`'s callback. Useful for upsampling control signals.
///
/// # Panics
///
/// Panics if `factor > max_factor`, or if `block`'s length is longer than the maximum block
/// size.
pub fn upsample_only<'a>(&'a mut self, block: &'a mut [f32], factor: usize) -> &'a mut [f32] {
assert!(factor <= self.stages.len());
// This is the 1x oversampling case, this should also modify the block to be consistent
if factor == 0 {
return block;
}
assert!(
block.len() <= self.stages[0].scratch_buffer.len() / 2,
"The block's size exceeds the maximum block size"
);
self.upsample_from(block, factor)
}
/// Upsample `block` through `factor` oversampling stages. Returns a reference to the
/// oversampled output stored in the last `LancZos3Stage`'s scratch buffer **with the correct
/// length**. This is a multiple of `block`'s length, which may be shorter than the entire
/// scratch buffer's length if `block` is shorter than the configured maximum block length.
///
/// # Panics
///
/// Panics if `block`'s length is longer than the maximum block size, if the number of
/// oversampling is smaller than `factor`, or if `factor` is zero. This is already checked for
/// in the process function.
fn upsample_from(&mut self, block: &[f32], factor: usize) -> &mut [f32] {
assert_ne!(factor, 0);
assert!(factor <= self.stages.len());
// The first stage is upsampled from `block`, and everything after that is upsampled from
// the stage preceeding it
self.stages[0].upsample_from(block);
let mut previous_upsampled_block_len = block.len() * 2;
for to_stage_idx in 1..factor {
// This requires splitting the vector so we can borrow the from-stage immutably and the
// to-stage mutably at the same time
let ([.., from], [to, ..]) = self.stages.split_at_mut(to_stage_idx) else { unreachable!() };
to.upsample_from(&from.scratch_buffer[..previous_upsampled_block_len]);
previous_upsampled_block_len *= 2;
}
&mut self.stages[factor - 1].scratch_buffer[..previous_upsampled_block_len]
}
/// Downsample starting from the `factor`th oversampling stage, writing the results from
/// downsampling the first stage to `block`. `block`'s actual length is taken into account to
/// compute the length of the oversampled blocks.
///
/// # Panics
///
/// Panics if `block`'s length is longer than the maximum block size, if the number of
/// oversampling is smaller than `factor`, or if `factor` is zero. This is already checked for
/// in the process function.
fn downsample_to(&mut self, block: &mut [f32], factor: usize) {
assert_ne!(factor, 0);
assert!(factor <= self.stages.len());
// This is the reverse of `upsample_from`. Starting from the last stage, the oversampling
// stages are downsampled to the previous stage and then the first stage is downsampled to
// `block`.
let mut next_downsampled_block_len = block.len() * 2usize.pow(factor as u32 - 1);
for to_stage_idx in (1..factor).rev() {
// This requires splitting the vector so we can borrow the from-stage immutably and the
// to-stage mutably at the same time
let ([.., to], [from, ..]) = self.stages.split_at_mut(to_stage_idx) else { unreachable!() };
from.downsample_to(&mut to.scratch_buffer[..next_downsampled_block_len]);
next_downsampled_block_len /= 2;
}
// And then the first stage downsamples to `block`
assert_eq!(next_downsampled_block_len, block.len());
self.stages[0].downsample_to(block);
}
}
impl Lanzcos3Stage {
/// Create a `stage_number`th oversampling stage, where `stage_number` is this stage's
/// zero-based index in a list of stages. Stage 0 handles the 2x oversampling, stage 1 handles
/// the 4x oversampling, stage 2 handles the 8x oversampling, etc.. This is used to make sure
/// the stage's effect on the total latency is always an integer amount.
///
/// The maximum block size is used to allocate enough scratch space for oversampling that many
/// samples *at the base sample rate*. The scratch buffer's size automatically takes the stage
/// number into account.
pub fn new(maximum_block_size: usize, stage_number: usize) -> Self {
let oversampling_amount = 2usize.pow(stage_number as u32 + 1);
// In theory we would only need to delay one of these, but we'll distribute the delay
// cleanly
assert!(LANCZOS3_UPSAMPLING_KERNEL.len() == LANCZOS3_DOWNSAMPLING_KERNEL.len());
assert!(LANCZOS3_UPSAMPLING_KERNEL.len() % 2 == 1);
// This is the latency of the upsampling and downsampling filter, at the base sample rate.
// Because this stage's filtering happens at a higher sample rate (`oversampling_amount`
// times the base sample rate), we need to make sure that the delay imposed _on this higher
// sample rate_ results in an integer amount of latency at the base sample rate. To do that,
// the delay needs to be divisible by `oversampling_amount`. This extra delay is only
// applied to the upsampling part to keep the downsampling simpler.
let uncompensated_stage_latency = LANZCOS3_KERNEL_LATENCY + LANZCOS3_KERNEL_LATENCY;
// Say the oversampling amount is 4, then an uncompensated stage latency of 8 results in 0
// additional samples of delay, 9 in 3, 10 in 2, 11 in 1, 12 in 0, etc. This is added to the
// upsampling filter.
let additional_delay_required = (-(uncompensated_stage_latency as isize))
.rem_euclid(oversampling_amount as isize)
as usize;
Self {
oversampling_amount,
upsampling_rb: vec![0.0; LANCZOS3_UPSAMPLING_KERNEL.len() + additional_delay_required],
upsampling_write_pos: 0,
additional_upsampling_latency: additional_delay_required,
downsampling_rb: [0.0; LANCZOS3_DOWNSAMPLING_KERNEL.len()],
downsampling_write_pos: 0,
scratch_buffer: vec![0.0; maximum_block_size * oversampling_amount],
}
}
pub fn reset(&mut self) {
// Resetting the positions is not needed, but it also doesn't hurt
self.upsampling_rb.fill(0.0);
self.upsampling_write_pos = 0;
self.downsampling_rb.fill(0.0);
self.downsampling_write_pos = 0;
}
/// The stage's effect on the oversampling's latency as a whole. This is already divided by the
/// stage's oversampling amount.
pub fn effective_latency(&self) -> u32 {
let uncompensated_stage_latency = LANZCOS3_KERNEL_LATENCY + LANZCOS3_KERNEL_LATENCY;
let total_stage_latency = uncompensated_stage_latency + self.additional_upsampling_latency;
let effective_latency = total_stage_latency as f32 / self.oversampling_amount as f32;
assert!(effective_latency.fract() == 0.0);
effective_latency as u32
}
/// Upsample `block` 2x and write the results to this stage's scratch buffer.
///
/// # Panics
///
/// Panics if `block`'s times two exceeds the scratch buffer's size.
pub fn upsample_from(&mut self, block: &[f32]) {
let output_length = block.len() * 2;
assert!(output_length <= self.scratch_buffer.len());
// We'll first zero-stuff the input, and then run that through the lanczos halfband filter
for (input_sample_idx, input_sample) in block.iter().enumerate() {
let output_sample_idx = input_sample_idx * 2;
self.scratch_buffer[output_sample_idx] = *input_sample;
self.scratch_buffer[output_sample_idx + 1] = 0.0;
}
// The zero-stuffed input is now run through the lanczos filter, which is a windowed sinc
// filter where every even tap has a value of zero. That means that if the filter is
// centered on a non-zero sample, the output must be equal to that sample and we can thus
// skip the convolution step entirely. Another important consideration is that we are
// imposing an additional `self.additional_upsampling_latency` samples of delay on the input
// to make sure the effective latency of the oversampling is always an integer amount.
let mut direct_read_pos =
(self.upsampling_write_pos + LANZCOS3_KERNEL_LATENCY) % self.upsampling_rb.len();
for output_sample_idx in 0..output_length {
// For a more intuitive description, imagine that `self.additional_upsampling_latency`
// is 2, and `self.upsampling_write_pos` is currently 0. For an 11-tap filter (like the
// lanczos3 kernel with the zero points removed from both ends), the situation after
// this statement would look like this:
//
// [n, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
// ^-- self.upsampling_write_pos
self.upsampling_rb[self.upsampling_write_pos] = self.scratch_buffer[output_sample_idx];
// The read/write head position needs to be incremented before filtering so that the
// just-added sample becomes the last sample in the ring buffer (if the additional
// latency/delay is 0)
self.upsampling_write_pos += 1;
if self.upsampling_write_pos == self.upsampling_rb.len() {
self.upsampling_write_pos = 0;
}
direct_read_pos += 1;
if direct_read_pos == self.upsampling_rb.len() {
direct_read_pos = 0;
}
// We can now read starting from the new `self.upsampling_write_pos`. This will cause
// the output to be delayed by `self.additional_upsampling_latency` samples. The range
// used for convolution is visualized below. It in this example it takes 2 additional
// iterations of this loop before sample `n` is considered again. Even output samples
// can directly be read from the ring buffer without convolution at the visualized
// offset.
//
// [n, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
// ^--------------^---------------^
// └- direct_read_position
//
// NOTE: 'Even samples' is considered from the perspective of a zero latency filter. In
// this case the evenness of the filter's latency also needs to be considered. If
// it's odd then the direct reading should also happen for odd indexed samples.
self.scratch_buffer[output_sample_idx] =
if output_sample_idx % 2 == (LANZCOS3_KERNEL_LATENCY % 2) {
nih_debug_assert_eq!(
self.upsampling_rb[(direct_read_pos + self.upsampling_rb.len() - 1)
% self.upsampling_rb.len()],
0.0
);
nih_debug_assert_eq!(
self.upsampling_rb[(direct_read_pos + 1) % self.upsampling_rb.len()],
0.0
);
self.upsampling_rb[direct_read_pos]
} else {
convolve_rb(
&self.upsampling_rb,
&LANCZOS3_UPSAMPLING_KERNEL,
self.upsampling_write_pos,
)
};
}
}
/// Downsample starting from the last oversampling stage, writing the results from downsampling
/// the first stage to `block`. `block`'s actual length is taken into account to compute the
/// length of the oversampled blocks.
///
/// # Panics
///
/// Panics if `block`'s divided by two exceeds the scratch buffer's size.
pub fn downsample_to(&mut self, block: &mut [f32]) {
let input_length = block.len() * 2;
assert!(input_length <= self.scratch_buffer.len());
// The additional delay to make the latency integer has already been taken into account in
// the upsampling part, so the downsampling is more straightforward
for input_sample_idx in 0..input_length {
self.downsampling_rb[self.downsampling_write_pos] =
self.scratch_buffer[input_sample_idx];
// The read/write head position needs to be incremented before filtering so that the
// just-added sample becomes the last sample in the ring buffer
self.downsampling_write_pos += 1;
if self.downsampling_write_pos == LANCZOS3_DOWNSAMPLING_KERNEL.len() {
self.downsampling_write_pos = 0;
}
// Because downsampling by a factor of two is filtering followed by decimation (where
// you take every even sample), we only need to compute the filtered output for the even
// samples. This is similar to how we only need to filter half the samples in the
// upsampling step.
if input_sample_idx % 2 == 0 {
let output_sample_idx = input_sample_idx / 2;
block[output_sample_idx] = convolve_rb(
&self.downsampling_rb,
// NOTE: This is `LANCZOS3_UPSAMPLING_KERNEL`, but with a factor two gain
// decrease to compensate for the 2x gain increase that happened during
// the upsampling
&LANCZOS3_DOWNSAMPLING_KERNEL,
self.downsampling_write_pos,
)
}
}
}
}
/// Convolve `input_ring_buffer` with `kernel`, with `input_ring_buffer` rotated so that it starts
/// at `ring_buffer_pos` and then wraps back around to the start.
///
/// # Panics
///
/// Assumes `input_ring_buffer` and `kernel` have the same length. May panic if they don't.
fn convolve_rb(input_ring_buffer: &[f32], kernel: &[f32], ring_buffer_pos: usize) -> f32 {
let mut total = 0.0;
nih_debug_assert!(input_ring_buffer.len() >= kernel.len());
// This is straightforward convolution. Could be implemented much more efficiently, but for our
// 11-tap filter this works fine
let num_samples_until_wraparound =
(input_ring_buffer.len() - ring_buffer_pos).min(kernel.len());
for (read_pos_offset, kernel_sample) in kernel
.iter()
.rev()
.take(num_samples_until_wraparound)
.enumerate()
{
total += kernel_sample * input_ring_buffer[ring_buffer_pos + read_pos_offset];
}
for (read_pos, kernel_sample) in kernel
.iter()
.rev()
// Needs to happen before the `enumerate`
.skip(num_samples_until_wraparound)
.enumerate()
{
total += kernel_sample * input_ring_buffer[read_pos];
}
total
}
#[cfg(test)]
mod tests {
use super::*;
mod convolve_rb {
use super::*;
#[test]
fn test_with_wrap() {
let input_rb = [1.0, 2.0, -3.0, 4.0];
let kernel = [1.0, 2.0, -0.0, -1.0];
let input_pos = 2;
// This should be `(-3.0 * -1.0) + (4.0 * 0.0) + (1.0 * 2.0) + (2.0 * 1.0) = 7.0`
let result = convolve_rb(&input_rb, &kernel, input_pos);
assert_eq!(result, 7.0);
}
#[test]
fn test_no_wrap() {
let input_rb = [1.0, 2.0, -3.0, 4.0];
let kernel = [1.0, 2.0, 0.0, -1.0];
let input_pos = 0;
// This should be `(1.0 * -1.0) + (2.0 * 0.0) + (-3.0 * 2.0) + (4.0 * 1.0) = 7.0`
let result = convolve_rb(&input_rb, &kernel, input_pos);
assert_eq!(result, -3.0);
}
}
mod oversampling {
use super::*;
fn argmax(iter: impl IntoIterator<Item = f32>) -> usize {
iter.into_iter()
.enumerate()
.max_by(|(_, value_a), (_, value_b)| value_a.total_cmp(value_b))
.unwrap()
.0
}
/// Makes sure that the reported latency is correct and is (more or less) an integer value
fn test_latency(oversampling_factor: usize) {
let mut delta_impulse = [0.0f32; 64];
delta_impulse[0] = 1.0;
let mut oversampler =
Lanczos3Oversampler::new(delta_impulse.len(), oversampling_factor);
let reported_latency = oversampler.latency(oversampling_factor) as usize;
assert!(
delta_impulse.len() > reported_latency,
"The delta impulse array is too small to test the latency at oversampling factor \
{oversampling_factor}, this is an error with the test case"
);
oversampler.process(&mut delta_impulse, oversampling_factor, |_| ());
let new_impulse_idx = argmax(delta_impulse);
assert_eq!(new_impulse_idx, reported_latency);
// The latency should also not be fractional
assert!(delta_impulse[new_impulse_idx] > delta_impulse[new_impulse_idx - 1]);
assert!(delta_impulse[new_impulse_idx] > delta_impulse[new_impulse_idx + 1]);
}
/// Checks whether the output matches the input when compensating for the latency. Also
/// applies a gain offset to make sure the process callback actually works.
fn test_sine_output(oversampling_factor: usize) {
// The gain applied to the oversampled version
const GAIN: f32 = 2.0;
// As a fraction of the sampling frequency
const FREQUENCY: f32 = 0.125;
let mut input = [0.0f32; 128];
for (i, sample) in input.iter_mut().enumerate() {
*sample = (i as f32 * (FREQUENCY * 2.0 * std::f32::consts::PI)).sin();
}
let mut output = input;
let mut oversampler = Lanczos3Oversampler::new(output.len(), oversampling_factor);
oversampler.process(&mut output, oversampling_factor, |upsampled| {
for sample in upsampled {
*sample *= GAIN;
}
});
let reported_latency = oversampler.latency(oversampling_factor) as usize;
for (input_sample_idx, input_sample) in input
.into_iter()
.enumerate()
.take(input.len() - reported_latency)
{
let output_sample_idx = input_sample_idx + reported_latency;
let output_sample = output[output_sample_idx];
// There can be quite a big difference between the input and output thanks to the
// filter's ringing
approx::assert_relative_eq!(input_sample * GAIN, output_sample, epsilon = 0.1);
}
}
#[test]
fn latency_2x() {
test_latency(1);
}
#[test]
fn latency_4x() {
test_latency(2);
}
#[test]
fn latency_8x() {
test_latency(3);
}
#[test]
fn latency_16x() {
test_latency(4);
}
#[test]
fn sine_output_2x() {
test_sine_output(1);
}
#[test]
fn sine_output_4x() {
test_sine_output(2);
}
#[test]
fn sine_output_8x() {
test_sine_output(3);
}
#[test]
fn sine_output_16x() {
test_sine_output(4);
}
}
}