integrated with envelopes

packages/superdough/helpers.mjs

@@ -103,14 +103,22 @@ export const getADSRValues = (params, curve = 'linear', defaultValues) => {
   return [Math.max(a ?? 0, envmin), Math.max(d ?? 0, envmin), Math.min(sustain, envmax), Math.max(r ?? 0, releaseMin)];
 };
 
-export function createFilter(context, type, frequency, Q, att, dec, sus, rel, fenv, start, end, fanchor) {
+export function createFilter(context, type, frequency, Q, att, dec, sus, rel, fenv, start, end, fanchor, model) {
   const curve = 'exponential';
   const [attack, decay, sustain, release] = getADSRValues([att, dec, sus, rel], curve, [0.005, 0.14, 0, 0.1]);
-  const filter = context.createBiquadFilter();
+  let filter;
+  let frequencyParam;
+  if (model === 'ladder') {
+    filter = getWorklet(context, 'ladder-processor', { frequency, q: Q, drive: 1 });
+    frequencyParam = filter.parameters.get('frequency');
+  } else {
+    filter = context.createBiquadFilter();
+    filter.type = type;
+    filter.Q.value = Q;
+    filter.frequency.value = frequency;
+    frequencyParam = filter.frequency;
+  }
 
-  filter.type = type;
-  filter.Q.value = Q;
-  filter.frequency.value = frequency;
   // envelope is active when any of these values is set
   const hasEnvelope = att ?? dec ?? sus ?? rel ?? fenv;
   // Apply ADSR to filter frequency
@@ -122,7 +130,7 @@ export function createFilter(context, type, frequency, Q, att, dec, sus, rel, fe
     let min = clamp(2 ** -offset * frequency, 0, 20000);
     let max = clamp(2 ** (fenvAbs - offset) * frequency, 0, 20000);
     if (fenv < 0) [min, max] = [max, min];
-    getParamADSR(filter.frequency, attack, decay, sustain, release, min, max, start, end, curve);
+    getParamADSR(frequencyParam, attack, decay, sustain, release, min, max, start, end, curve);
     return filter;
   }
   return filter;

packages/superdough/superdough.mjs

@@ -395,6 +395,8 @@ export const superdough = async (value, t, hapDuration) => {
   chain.push(gainNode(gain));
 
   if (cutoff !== undefined) {
+    // chain.push(getWorklet(ac, 'ladder-processor', { frequency: cutoff, q: resonance, drive: 1 }));
+
     let lp = () =>
       createFilter(
         ac,
@@ -409,6 +411,7 @@ export const superdough = async (value, t, hapDuration) => {
         t,
         t + hapDuration,
         fanchor,
+        ftype,
       );
     chain.push(lp());
     if (ftype === '24db') {
@@ -471,8 +474,6 @@ export const superdough = async (value, t, hapDuration) => {
   shape !== undefined && chain.push(getWorklet(ac, 'shape-processor', { shape, postgain: shapevol }));
   distort !== undefined && chain.push(getWorklet(ac, 'distort-processor', { distort, postgain: distortvol }));
 
-  chain.push(getWorklet(ac, 'ladder-processor', { cutoff: 500, q: 1 }));
-
   compressorThreshold !== undefined &&
     chain.push(
       getCompressor(ac, compressorThreshold, compressorRatio, compressorKnee, compressorAttack, compressorRelease),

packages/superdough/worklets.mjs

@@ -115,8 +115,9 @@ const _PI = 3.14159265359;
 class LadderProcessor extends AudioWorkletProcessor {
   static get parameterDescriptors() {
     return [
-      { name: 'cutoff', defaultValue: 500 },
+      { name: 'frequency', defaultValue: 500 },
       { name: 'q', defaultValue: 1 },
+      { name: 'drive', defaultValue: 1 },
     ];
   }
 
@@ -143,11 +144,13 @@ class LadderProcessor extends AudioWorkletProcessor {
     this.started = hasInput;
 
     const resonance = parameters.q[0];
-    let cutoff = parameters.cutoff[0];
+    const drive = parameters.drive[0] * 2;
+    let cutoff = parameters.frequency[0];
     cutoff = (cutoff * 2 * _PI) / sampleRate;
     cutoff = cutoff > 1 ? 1 : cutoff;
 
     const k = resonance * 4;
+    const makeupgain = 1 / drive;
 
     for (let n = 0; n < blockSize; n++) {
       for (let i = 0; i < input.length; i++) {
@@ -157,12 +160,12 @@ class LadderProcessor extends AudioWorkletProcessor {
         this.p33 = this.p32;
         this.p32 = this.p3;
 
-        this.p0 += (fast_tanh(input[i][n] - k * out) - fast_tanh(this.p0)) * cutoff;
+        this.p0 += (fast_tanh(input[i][n] * drive - k * out) - fast_tanh(this.p0)) * cutoff;
         this.p1 += (fast_tanh(this.p0) - fast_tanh(this.p1)) * cutoff;
         this.p2 += (fast_tanh(this.p1) - fast_tanh(this.p2)) * cutoff;
         this.p3 += (fast_tanh(this.p2) - fast_tanh(this.p3)) * cutoff;
 
-        output[i][n] = out;
+        output[i][n] = out * makeupgain;
       }
     }
     return true;