[threebandeq] implement everything
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README.md
13
README.md
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@ -54,7 +54,7 @@ the following is the current list of plugins
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- multidim: randomized midi note generator
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- robotuna: automatic pitch correction
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- hysteria: hysteresis nonlinear effect
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- threebandeq: [WIP] 3 band eq
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- threebandeq: 3 band eq
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### basic_gain
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@ -134,7 +134,16 @@ turn the values up for loud and distorted. don't set them all to the max at once
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### threebandeq [WIP]
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wip, there's nothing yet
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simple three band equalizer
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parameters:
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- `low band`: frequency at which the low band ends
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- `high band`: frequency at which the high band starts
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- `low gain`: gain for the low band
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- `mid gain`: gain for the mid band
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- `high gain`: gain for the high band
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low band goes from 0hz to `low band`, mid band goes from `low band` to `high band`, high band goes from `high band` to half the sample rate
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## contributing
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@ -1,31 +1,122 @@
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#![allow(incomplete_features)]
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#![feature(generic_associated_types)]
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// https://www.musicdsp.org/en/latest/Filters/236-3-band-equaliser.html
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use baseplug::{Plugin, ProcessContext};
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use serde::{Deserialize, Serialize};
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baseplug::model! {
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#[derive(Debug, Serialize, Deserialize)]
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struct ThreeBandEqModel {
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#[model(min = -90.0, max = 3.0)]
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#[parameter(name = "gain", unit = "Decibels",
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gradient = "Power(0.15)")]
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gain: f32
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#[model(min = 0.0, max = 48000.0)]
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#[parameter(name = "low band")]
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low_band: f32,
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#[model(min = 0.0, max = 48000.0)]
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#[parameter(name = "mid band")]
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high_band: f32,
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#[model(min = 0.0, max = 3.0)]
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#[parameter(name = "low gain")]
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low_gain: f32,
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#[model(min = 0.0, max = 3.0)]
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#[parameter(name = "mid gain")]
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mid_gain: f32,
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#[model(min = 0.0, max = 3.0)]
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#[parameter(name = "high gain")]
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high_gain: f32,
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}
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}
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impl Default for ThreeBandEqModel {
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fn default() -> Self {
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Self {
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// "gain" is converted from dB to coefficient in the parameter handling code,
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// so in the model here it's a coeff.
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// -0dB == 1.0
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gain: 1.0,
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low_band: 800.0,
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high_band: 5000.0,
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low_gain: 1.0,
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mid_gain: 1.0,
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high_gain: 1.0,
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}
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}
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}
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struct ThreeBandEq;
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#[derive(Default)]
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struct Eq {
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// Filter #1 (Low band)
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f1p0: f32, // Poles ...
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f1p1: f32,
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f1p2: f32,
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f1p3: f32,
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// Filter #2 (High band)
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f2p0: f32, // Poles ...
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f2p1: f32,
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f2p2: f32,
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f2p3: f32,
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// Sample history buffer
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sdm1: f32, // Sample data minus 1
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sdm2: f32, // 2
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sdm3: f32, // 3
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}
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impl Eq {
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#[allow(clippy::too_many_arguments)]
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fn process(
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&mut self,
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input: f32,
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lf: f32,
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hf: f32,
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lg: f32,
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mg: f32,
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hg: f32,
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sample_rate: f32,
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) -> f32 {
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let lf = 2.0 * (std::f32::consts::PI * (lf / sample_rate)).sin();
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let hf = 2.0 * (std::f32::consts::PI * (hf / sample_rate)).sin();
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// Filter #1 (lowpass)
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self.f1p0 += lf * (input - self.f1p0) + VSA;
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self.f1p1 += lf * (self.f1p0 - self.f1p1);
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self.f1p2 += lf * (self.f1p1 - self.f1p2);
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self.f1p3 += lf * (self.f1p2 - self.f1p3);
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let l = self.f1p3;
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// Filter #2 (highpass)
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self.f2p0 += hf * (input - self.f2p0) + VSA;
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self.f2p1 += hf * (self.f2p0 - self.f2p1);
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self.f2p2 += hf * (self.f2p1 - self.f2p2);
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self.f2p3 += hf * (self.f2p2 - self.f2p3);
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// we subtract from sdm3 because the filter has a three sample delay
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let h = self.sdm3 - self.f2p3;
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// Calculate midrange (signal - (low + high))
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let m = self.sdm3 - (h + l);
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// Scale
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let l = l * lg;
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let m = m * mg;
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let h = h * hg;
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// Shuffle history buffer
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self.sdm3 = self.sdm2;
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self.sdm2 = self.sdm1;
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self.sdm1 = input;
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l + m + h
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}
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}
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#[derive(Default)]
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struct ThreeBandEq {
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l_eq: Eq,
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r_eq: Eq,
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}
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/// Very small amount
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const VSA: f32 = 1.0 / 4294967295.0;
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impl Plugin for ThreeBandEq {
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const NAME: &'static str = "basic gain";
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@ -39,7 +130,8 @@ impl Plugin for ThreeBandEq {
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#[inline]
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fn new(_sample_rate: f32, _model: &ThreeBandEqModel) -> Self {
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Self
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// Default cause we want to start with 0s in everything
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Self::default()
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}
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#[inline]
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@ -47,9 +139,20 @@ impl Plugin for ThreeBandEq {
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let input = &ctx.inputs[0].buffers;
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let output = &mut ctx.outputs[0].buffers;
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let sr = ctx.sample_rate;
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for i in 0..ctx.nframes {
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output[0][i] = input[0][i] * model.gain[i];
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output[1][i] = input[1][i] * model.gain[i];
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// frequencies
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let lf = model.low_band[i];
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let hf = model.high_band[i];
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// gains
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let lg = model.low_gain[i];
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let mg = model.mid_gain[i];
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let hg = model.high_gain[i];
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output[0][i] = self.l_eq.process(input[0][i], lf, hf, lg, mg, hg, sr);
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output[1][i] = self.r_eq.process(input[1][i], lf, hf, lg, mg, hg, sr);
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}
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}
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}
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