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July 10, 2026 · View on GitHub

Canonical audio filter implementations.

Analog
_{Moog ladder · Diode ladder · Korg35 · Oberheim}

Effect
_{DC blocker · Comb · Allpass · Pre-emphasis · Derivative / integral · Lowpass · Highpass · Bandpass · Notch · Resonator · Spectral tilt · Variable bandwidth}

Biquad kernel
_{shared RBJ coefficients + SOS state, underlies every biquad-shaped atom above}

Moved elsewhere (Weighting · Auditory · EQ) — see each section for the new package.

Install

npm install @audio/filter

// import everything
import * as filter from '@audio/filter'

// import by domain
import { aWeighting, kWeighting } from '@audio/weighting'
import { gammatone, melBank } from '@audio/auditory'
import { moogLadder, oberheim } from '@audio/filter'
import vocoder from '@audio/speech-vocoder'
import { lpcAnalysis } from '@audio/speech-lpc'
import { parametricEq, crossover, baxandall, tilt, lowShelf, highShelf } from '@audio/eq'
import { dcBlocker, notch, lowpass, highpass, bandpass, resonator } from '@audio/filter'

API

Most filters share one shape:

filter(buffer, params)   // → buffer (modified in-place)

Takes an Array/Float32Array/Float64Array, modifies it in-place, returns it. Pass the same params object on every call to persist state across blocks automatically:

import { moogLadder } from '@audio/filter'

let params = { fc: 1000, resonance: 0.5, fs: 44100 }
for (let buf of stream) moogLadder(buf, params)

Three families, by shape:

In-place, single buffer — filter(buffer, params) → buffer. The majority: weighting, analog, effect, most of EQ.
In-place, dual buffer — filter(bufA, bufB, params) → { ... }. crossfeed(left, right, params), vocoder(carrier, modulator, params) — two channels/signals interact, so neither buffer alone is the whole story.
Designers/analyzers — take no buffer, return descriptor/SOS data instead of processing anything: octaveBank, erbBank, barkBank, melBank, crossover, lpcAnalysis. Feed their output to digital-filter's filter() or to lpcSynthesize.

For frequency analysis, weighting filters expose a .coefs(fs) method returning a second-order sections (SOS) array — [{b0, b1, b2, a1, a2}, ...], one biquad per section — for use with digital-filter:

import { aWeighting } from '@audio/weighting'
import { freqz, mag2db } from 'digital-filter'

let sos  = aWeighting.coefs(44100)
let resp = freqz(sos, 2048, 44100)
let db   = mag2db(resp.magnitude)

Weighting

Standard measurement curves (A/C/K/ITU-468/RIAA) — moved to their own repo in the 2026-07 family split.

Now lives at: github.com/audiojs/weighting · npm install @audio/weighting
Migration: import { aWeighting } from '@audio/filter' → import { aWeighting } from '@audio/weighting'

Auditory

Cochlear/auditory-system models (gammatone, octave/ERB/Bark/mel banks) — moved to their own repo in the 2026-07 family split.

Now lives at: github.com/audiojs/auditory · npm install @audio/auditory
Migration: import { gammatone } from '@audio/filter' → import { gammatone } from '@audio/auditory'

Analog

Discrete-time models of analog circuits — each named after the hardware it replicates. Nonlinear, stateful, process in-place. The filters in synthesizers.

Resonance means something different per filter — all four take resonance in 0–1, but the mapping and self-oscillation point differ: moogLadder maps $k = 4 \cdot \text{resonance}$ , self-oscillates exactly at resonance=1 (Stilson & Smith 1996, <0.5% frequency error); diodeLadder uses the same $k = 4 \cdot \text{resonance}$ but self-oscillation shifts to ≈1.15–1.2 because its extra per-stage $\tanh$ adds damping; korg35 maps $k = 2 \cdot \text{resonance}$ and never self-oscillates at any value — 2 real poles can't reach the −180° loop phase Barkhausen's criterion needs at any finite, audible frequency, so resonance only adds damping/saturation character; oberheim maps $R = 1 - \text{resonance}$ (inverted — resonance=1 gives $R=0$ , maximum SVF resonance), with bandpass peak gain exactly $1/(2R) $.<br> **`drive`** (default 1, all four filters) is input gain into each filter's$ \tanh $saturation path — `drive: 0` mutes the signal ($ \tanh(0) = 0$) rather than disabling saturation and falling back to a linear filter.

Moog ladder

Robert Moog's 4-pole transistor ladder, 1965 — the most imitated filter in electronic music.

Circuit: 4 cascaded one-pole transistor ladder sections, global feedback from output to input
Implementation: Zero-delay feedback (ZDF) via trapezoidal integration — Zavalishin (2012)¹, Ch. 6
Response: $-24\,\text{dB/oct}$ lowpass; resonance peak at $f_c$ ; self-oscillation (sine wave) at resonance=1
Nonlinearity: $\tanh$ saturation at input (transistor ladder characteristic)

import { moogLadder } from '@audio/filter'

let params = { fc: 800, resonance: 0.7, fs: 44100 }
moogLadder(buffer, params)

// Self-oscillation — runs indefinitely from a single impulse
let silent = new Float64Array(4096); silent[0] = 0.01
moogLadder(silent, { fc: 1000, resonance: 1, fs: 44100 })

Patent: Moog (1965) US3475623²
vs Diode ladder: Moog saturates only at input; diode saturates at each stage — different character at high resonance

Moog ladder resonance sweep

Diode ladder

Roland TB-303 / EMS VCS3 style — per-stage saturation gives the characteristic acid "squelch".

Circuit: Roland TB-303, EMS VCS3, EDP Wasp
Key difference from Moog: stages are bidirectionally coupled — no unity-gain buffers between them (unlike Moog), so each stage loads its neighbors; solved as one tridiagonal system per sample (Thomas algorithm) instead of Moog's simple forward cascade
Character: preserves more bass at high resonance than Moog — closed-form DC gain $1/(1 + 4\cdot\text{resonance}) $vs Moog's \$ 1/(1 + 4\cdot\text{resonance}\cdot(1+G+G^2+G^3+G^4)) $,$ G<1 $— and more "squelchy"/aggressive due to the per-stage$ \tanh $<br> **Implementation**: ZDF — Zavalishin (2012)[^9]; Pirkle (2019)[^11], Ch. 10<br> **Stability**: bounded (per-stage$ \tanh $-saturated) across the documented 0–1 range and well beyond; self-oscillates near resonance≈1.15–1.2 — higher than Moog's exact 1, since the extra per-stage$ \tanh$ adds damping

import { diodeLadder } from '@audio/filter'

let params = { fc: 500, resonance: 0.8, fs: 44100 }
diodeLadder(buffer, params)

Diode ladder

Korg35

Korg MS-10/MS-20, 1978 — 2-pole filter with lowpass and highpass outputs from nonlinear feedback.

Topology: 2 cascaded one-pole sections with nonlinear feedback; HP = input − LP
Response: $-12\,\text{dB/oct}$ ; resonance 0–1 adds damping/saturation character — no resonant peak, no self-oscillation at any setting (2 real poles in the loop can't reach the −180° phase Barkhausen's criterion needs at any finite, audible frequency, unlike Moog/Diode's 4-pole loops)
Complementarity: LP+HP=input holds exactly only at resonance=0 (verified to 1e-16); at resonance>0 the HP tap's extra feedback term makes LP+HP diverge from the input (measured −7.5 dB to +1.4 dB error at resonance=0.8 across 200 Hz–5 kHz)

import { korg35 } from '@audio/filter'

korg35(buffer, { fc: 1000, resonance: 0.5, type: 'lowpass',  fs: 44100 })
korg35(buffer, { fc: 1000, resonance: 0.5, type: 'highpass', fs: 44100 })

Circuit: Korg MS-10/MS-20 (1978)
Analysis: Stilson & Smith (1996)³; Zavalishin (2012)¹, Ch. 5
vs Moog ladder: 2-pole ( $-12\,\text{dB/oct}$ ) vs 4-pole ( $-24\,\text{dB/oct}$ ); Korg35 has both LP and HP taps from one circuit, but no self-oscillation

Korg35 LP and HP

Oberheim

Oberheim SEM (1974) — 2-pole state-variable filter with four modes from one circuit.

Topology: 2 trapezoidal integrators with nonlinear feedback; multimode output (LP/HP/BP/notch)
Response: $-12\,\text{dB/oct}$ ; warm, musical resonance; continuous mode morphing
Implementation: ZDF — Zavalishin (2012)¹, Ch. 4–5; $\tanh$ saturation on integrator states

import { oberheim } from '@audio/filter'

oberheim(buffer, { fc: 1000, resonance: 0.5, type: 'lowpass',  fs: 44100 })
oberheim(buffer, { fc: 1000, resonance: 0.5, type: 'highpass', fs: 44100 })
oberheim(buffer, { fc: 1000, resonance: 0.5, type: 'bandpass', fs: 44100 })
oberheim(buffer, { fc: 1000, resonance: 0.5, type: 'notch',    fs: 44100 })

Circuit: Oberheim SEM (1974), Two Voice, Four Voice, Eight Voice
vs Moog/Korg: 2-pole like Korg35 but true state-variable topology; LP/HP/BP/notch from one circuit; warmer resonance character

Oberheim SEM

Speech

Filters that model or process the human vocal tract — from vowel synthesis to spectral voice coding.

Formant

Parallel resonator bank — each peak models one vocal tract resonance (formant).

Model: parallel combination of second-order resonators, each modeling one vocal tract mode
Formant frequencies: determined by vocal tract shape; F1 controls vowel openness, F2 controls front/back
Typical ranges: F1: 250–850 Hz, F2: 850–2500 Hz, F3: 1700–3500 Hz
Implementation: uses resonator internally — constant peak-gain bandpass per formant
Defaults: F1=730 Hz, F2=1090 Hz, F3=2440 Hz (open vowel /a/)

import formant from '@audio/speech-formant'

formant(excitation, { fs: 44100 })   // vowel /a/ (default)

formant(excitation, {
  formants: [{ fc: 270, bw: 60, gain: 1 }, { fc: 2290, bw: 90, gain: 0.5 }],
  fs: 44100
})   // vowel /i/

Use when: speech synthesis, singing synthesis, vocal effects, acoustic phonetics
Not a substitute for: LPC synthesis, which estimates formants automatically from a speech signal

Formant filter

Vocoder

Channel vocoder — transfers the spectral envelope of one sound onto the pitched content of another.

Note: takes two separate buffers, returns a new buffer (does not modify in-place).

Principle: analyze modulator into N bands → extract envelope per band → multiply with filtered carrier → sum
Implementation: N parallel bandpass filters on both signals; envelope follower per modulator band
Band count: 8 = robotic effect; 16 = classic vocoder sound; 32+ = more speech intelligibility

import vocoder from '@audio/speech-vocoder'

// carrier: pitched source (sawtooth, buzz, noise...)
// modulator: signal whose spectral shape to impose (voice, instrument...)
let output = vocoder(carrier, modulator, { bands: 16, fs: 44100 })

Inventor: Dudley (1939)⁴, Bell Labs
Use when: voice effects, talkbox simulation, cross-synthesis, spectral morphing

LPC

Linear Predictive Coding — estimates the vocal tract transfer function from a speech signal.

Analysis: autocorrelation method + Levinson-Durbin recursion → LPC coefficients + residual
Synthesis: all-pole filter reconstructs signal from residual excitation
Round-trip: lpcAnalysis → lpcSynthesize recovers the original signal exactly

import { lpcAnalysis, lpcSynthesize } from '@audio/speech-lpc'

// Analysis: extract vocal tract model
let { coefs, gain, residual } = lpcAnalysis(speechFrame, { order: 12 })

// Synthesis: reconstruct from residual
lpcSynthesize(residual, { coefs, gain })   // residual → reconstructed speech

// Modify pitch: replace residual with different excitation
let buzz = generatePulseTrainAtNewPitch()
lpcSynthesize(buzz, { coefs, gain })       // speech at new pitch

Origin: Atal & Hanauer (1971)⁵; foundation of CELP, GSM, and modern speech codecs
Use when: speech coding, pitch modification, voice conversion, formant estimation, speech analysis

LPC analysis/synthesis

EQ

Studio EQ (graphic, parametric, crossover, shelving, Baxandall, tilt) — moved to its own repo in the 2026-07 family split. Crossfeed moved separately, to spatial.

EQ now lives at: github.com/audiojs/eq · npm install @audio/eq
Crossfeed now lives at: github.com/audiojs/spatial · npm install @audio/spatial (atom: @audio/spatial-crossfeed)
Migration: import { parametricEq, crossfeed } from '@audio/filter' → import { parametricEq } from '@audio/eq' + import { crossfeed } from '@audio/spatial'

Effect

Signal conditioning and spectral shaping — single-purpose filters with well-defined transfer functions.

DC blocker

Removes DC offset — the simplest useful filter.

$H(z) = \dfrac{1 - z^{-1}}{1 - Rz^{-1}}$

Topology: zero at $z = 1$ (DC), pole at $z = R$
Cutoff: $f_c \approx \frac{(1-R) f_s}{2\pi}$ — $R = 0.995$ gives ~35 Hz at 44.1 kHz

import { dcBlocker } from '@audio/filter'

let params = { R: 0.995 }
dcBlocker(buffer, params)

Use when: removing DC bias before processing, preventing lowpass filter saturation

DC blocker

Comb filter

Adds a delayed copy of the signal to itself — notches and peaks at harmonics of $f_s / D$ .

Feedforward: $H(z) = 1 + g \cdot z^{-D}$ — dips at $f = \frac{(2k+1) f_s}{2D}$ , depth depends on $g$ : a true null only at $|g|=1$ ; the default $g=0.5$ gives a $-6\,\text{dB}$ dip, not a notch
Feedback: $H(z) = \dfrac{1}{1 - g \cdot z^{-D}}$ — peaks at $f = \frac{k \cdot f_s}{D}$

import { comb } from '@audio/filter'

comb(buffer, { delay: 100, gain: 0.6, type: 'feedback' })

Use when: flanging, chorus (with modulated delay), Karplus-Strong string synthesis, room mode modeling

Comb filter

Allpass

Unity magnitude at all frequencies — shifts phase only. First and second order.

First order: $H(z) = \dfrac{a + z^{-1}}{1 + a z^{-1}}$ — pole at $z = -a$ , 180° phase shift at Nyquist
Second order: $H(z) = \dfrac{(1-\alpha) - 2\cos(\omega_0)z^{-1} + (1+\alpha)z^{-2}}{(1+\alpha) - 2\cos(\omega_0)z^{-1} + (1-\alpha)z^{-2}}$ , $\alpha = \sin(\omega_0)/(2Q)$ — RBJ cookbook allpass; 360° phase shift around $\omega_0$ , width of the transition controlled by $Q$

import { allpass } from '@audio/filter'

allpass.first(buffer, { a: 0.5 })                          // coefficient a
allpass.second(buffer, { fc: 1000, Q: 1, fs: 44100 })      // center fc, quality Q

Use when: phase equalization, reverb building blocks (Schroeder reverb), stereo widening

Allpass 2nd order

Pre-emphasis / de-emphasis

First-order highpass (emphasis) and its inverse (de-emphasis) — used before and after coding or transmission.

$H(z) = 1 - \alpha z^{-1}$ (emphasis) / $H(z) = \dfrac{1}{1 - \alpha z^{-1}}$ (de-emphasis)

Rolloff: emphasis boosts above $f_c = \frac{(1-\alpha) f_s}{2\pi}$ — $\alpha = 0.97$ gives ~210 Hz at 44.1 kHz
Inverse pair: deemphasis exactly cancels emphasis — $H_e(z) \cdot H_d(z) = 1$ (round-trip error ~5.5e-17)

import { emphasis, deemphasis } from '@audio/filter'

emphasis(buffer, { alpha: 0.97 })    // before encoding
deemphasis(buffer, { alpha: 0.97 })  // after decoding — exact inverse

Use when: speech coding (GSM, AMR uses $\alpha = 0.97$ ), tape recording, FM broadcasting

Pre-emphasis De-emphasis

Derivative / integral

First-difference and running-sum — an exact inverse pair (FFmpeg's aderivative/aintegral).

$H_d(z) = 1 - z^{-1}$ (derivative) / $H_i(z) = \dfrac{1}{1 - z^{-1}}$ (integral, leak=1)

Inverse pair: integral at leak: 1 exactly reconstructs the input passed to derivative — the running sum telescopes
Leak: leak < 1 bleeds the accumulator per sample so DC-biased material converges to a finite value ( $\frac{x_{dc}}{1-\text{leak}}$ ) instead of drifting

import { derivative, integral } from '@audio/filter'

derivative(buffer, {})
integral(buffer, { leak: 1 })    // 1 = exact FFmpeg aintegral; <1 for stability on real program material

Use when: edge/transient detection (derivative), reconstructing a signal from its differenced form (integral), envelope-adjacent accumulation

Lowpass

Removes everything above cutoff frequency — the most common filter in audio.

Order 2 (default): RBJ biquad lowpass — $-12\,\text{dB/oct}$
Order 4+: Butterworth cascaded SOS — $-6n\,\text{dB/oct}$ where $n$ = order; requires registering digital-filter's Butterworth designer once (kept out of the default import so lowpass/highpass stay lean when you only need order 2)

import { lowpass } from '@audio/filter'
import butterworth from 'digital-filter/iir/butterworth.js'

lowpass.useButterworth(butterworth)   // once, before any order > 2 call

lowpass(buffer, { fc: 2000, fs: 44100 })                // 2nd-order (default) — no registration needed
lowpass(buffer, { fc: 2000, order: 4, fs: 44100 })      // 4th-order Butterworth
lowpass(buffer, { fc: 2000, Q: 1.5, fs: 44100 })        // resonant

Use when: anti-aliasing, smoothing, removing hiss, synth subtractive filtering
vs Moog ladder: lowpass is a clean linear filter; Moog adds nonlinear saturation and self-oscillation

Lowpass

Highpass

Removes everything below cutoff frequency — DC removal, rumble elimination.

Order 2 (default): RBJ biquad highpass — $-12\,\text{dB/oct}$
Order 4+: Butterworth cascaded SOS — $-6n\,\text{dB/oct}$ where $n$ = order; same registration as Lowpass above

import { highpass } from '@audio/filter'
import butterworth from 'digital-filter/iir/butterworth.js'

highpass.useButterworth(butterworth)   // once, before any order > 2 call

highpass(buffer, { fc: 80, fs: 44100 })                 // rumble filter
highpass(buffer, { fc: 80, order: 4, fs: 44100 })       // steeper rolloff

Use when: removing rumble, mic handling noise, subsonic content before dynamics processing
vs DC blocker: highpass has adjustable cutoff and slope; DC blocker is simpler, lighter, fixed near 0 Hz

Highpass

Bandpass

Passes frequencies around center frequency, rejects the rest — constant 0 dB peak gain.

Implementation: RBJ biquad bandpass (constant peak, Q controls width)

import { bandpass } from '@audio/filter'

bandpass(buffer, { fc: 1000, Q: 5, fs: 44100 })         // narrow
bandpass(buffer, { fc: 1000, Q: 0.5, fs: 44100 })       // wide

Use when: isolating frequency regions, radio effect, walkie-talkie simulation, band splitting
vs Resonator: bandpass is RBJ biquad (standard); resonator has constant peak gain — use resonator for modal synthesis

Bandpass

Resonator

Constant peak-gain bandpass — peak amplitude stays fixed regardless of bandwidth.

$H(z) = \dfrac{\frac{1-R^2}{2}(1 - z^{-2})}{1 - 2R\cos(\omega_0)z^{-1} + R^2 z^{-2}}$

Pole radius: $R = e^{-\pi \cdot bw / f_s}$ — controls bandwidth; $bw \to 0$ gives infinite Q
Peak gain: always 0 dB by construction — the two zeros at $z = \pm 1$ shape the numerator so $(1-R^2)/2$ normalizes the pole peak regardless of fc/bw (verified ±0.01 dB across fc ∈ {50, 440, 5000, 15000} Hz, bw ∈ {5, 20, 200} Hz)
Origin: Julius O. Smith III, "Introduction to Digital Filters" — Two-Pole, "Constant Peak-Gain Resonator"⁶

import { resonator } from '@audio/filter'

resonator(buffer, { fc: 440, bw: 20, fs: 44100 })

Use when: additive synthesis (bells, gongs), modal synthesis, formant bank building
vs Peaking EQ: resonator has fixed 0 dB peak; peaking EQ has variable gain — use resonator for synthesis, EQ for mixing

Resonator

Notch

Band-reject filter — unity gain everywhere except a deep null at fc.

$H(z) = \dfrac{1 - 2\cos(\omega_0)z^{-1} + z^{-2}}{1 - 2\cos(\omega_0)z^{-1}(1-\alpha) + (1-2\alpha)z^{-2}}$

Q: controls notch width — $Q = 30$ is narrow (hum removal); $Q = 5$ is wider (resonance suppression)
Zeros: on the unit circle at $\pm\omega_0$ — exact null, independent of Q

import { notch } from '@audio/filter'

notch(buffer, { fc: 50,   Q: 30, fs: 44100 })   // remove 50 Hz mains hum
notch(buffer, { fc: 1000, Q: 10, fs: 44100 })   // suppress a resonance

Use when: mains hum removal (50/60 Hz), feedback cancellation, room mode suppression
vs Parametric EQ with negative gain: notch reaches −∞ dB exactly at fc; peaking EQ has finite attenuation

Notch

Pink noise

Shapes white noise to $1/f$ spectrum — moved to @audio/synth (the @audio/synth-noise atom) in the 2026-07 family split; spectralTilt below (still here) is the general-purpose fractional-slope tool.

Now lives at: github.com/audiojs/synth · npm install @audio/synth
Migration: import { pinkNoise } from '@audio/filter' → import { pinkNoise } from '@audio/synth'

Spectral tilt

Applies a constant dB/octave slope — tilts the entire spectrum.

Model: cascade of 8 octave-spaced first-order shelving sections approximating a fractional power-law spectrum $S(f) \propto f^\alpha$ ; positive slope boosts highs, negative slope cuts them
slope: $\alpha = -3\,\text{dB/oct}$ gives pink noise character; $-6\,\text{dB/oct}$ gives brownian/red noise
Accuracy: measured slope tracks the requested dB/oct to within ~20% (inherent to the 8-stage shelving cascade) — e.g. slope: +3 measures ≈+2.4 dB/oct, slope: -6 measures ≈-4.9 dB/oct

import { spectralTilt } from '@audio/filter'

spectralTilt(buffer, { slope: -3, fs: 44100 })   // −3 dB/oct: pink noise character
spectralTilt(buffer, { slope: +3, fs: 44100 })   // +3 dB/oct: pre-emphasis for coding

Use when: matching microphone/speaker frequency responses, spectral coloring, noise synthesis

Spectral tilt

Variable bandwidth

Lowpass with continuously variable bandwidth — smooth parameter automation without discontinuities.

Implementation: fc/Q are exponentially smoothed toward their target values with a 5 ms time constant, and biquad coefficients are recomputed from the smoothed values every sample (not once per buffer)
Property: no discontinuity when $f_c$ or $Q$ change — a mid-buffer step change lands ~9x closer to the plain-lowpass baseline than an abrupt coefficient jump; once converged, steady-state output equals plain lowpass/highpass/bandpass

import { variableBandwidth } from '@audio/filter'

variableBandwidth(buffer, { fc: 2000, Q: 1.0, fs: 44100 })

Use when: LFO-modulated filter cutoff, automated EQ sweeps, smooth filter animation
vs Direct biquad: recalculating biquad coefficients per sample causes zipper noise; variable bandwidth avoids this

Variable bandwidth

Biquad kernel

highpass/lowpass/bandpass/notch/allpass above are built on @audio/filter-biquad, which itself wraps the shared @audio/biquad kernel — one coefficient/state source for every biquad-shaped atom in the @audio ecosystem (RBJ cookbook coefficients, Web Audio conventions, transposed direct-form-II state).

import { lowpass, peaking, filter, process, state, cascade, magnitude } from '@audio/biquad'

let c = lowpass(1000, 0.707, 44100)   // coefficients — normalized a0 = 1
filter(chunk, { coefs: [c] })         // params-convention: state rides the params object

let s = process(chunk, c, state())    // section-level kernel, explicit state
magnitude(c, 440, 44100)              // |H(f)| — analysis/plotting

Use when: building a new biquad-shaped atom, or bypassing filter-biquad's Hz/Q wrapper for raw coefficient/state control
See: github.com/audiojs/filter/tree/main/packages/biquad

Filter selection guide

I need to...	Use
Synth filter — warmth and resonance	`moogLadder`
Synth filter — acid / squelch	`diodeLadder`
Synth filter — 2-pole LP + HP	`korg35`
Synth filter — multimode SVF	`oberheim`
Synthesize vowel sounds	`formant`
Transfer one sound's spectral shape to another	`vocoder`
Analyze/resynthesize speech, change pitch	`lpcAnalysis` / `lpcSynthesize`
Remove DC offset	`dcBlocker`
Remove mains hum / suppress resonance	`notch`
Clean lowpass / anti-alias	`lowpass`
Remove rumble / subsonic content	`highpass`
Isolate a frequency band	`bandpass`
Create resonant combing	`comb`
Phase-shift without changing magnitude	`allpass.first`, `allpass.second`
Pre-process for audio coding	`emphasis` / `deemphasis`
Modal synthesis (bells, drums, rooms)	`resonator`
Tilt spectrum for noise synthesis	`spectralTilt`
Smooth automated filter sweeps	`variableBandwidth`
Build a custom biquad-shaped atom	`@audio/biquad` kernel (coefficients + SOS state)

Measurement curves, auditory banks, studio EQ, crossfeed, and colored noise moved out of this repo — see Weighting, Auditory, EQ, and each section's pointer for the replacement package.

FAQ

Why does my filter click when I change fc or Q? Biquad coefficients change discontinuously between samples. Use variableBandwidth for smooth automated sweeps, or crossfade.

Why does my Moog/Diode filter blow up? resonance=1 on Moog is intentional self-oscillation. Diode ladder is bounded across its full resonance range (per-stage $\tanh$ -saturated); it self-oscillates near resonance≈1.15–1.2, not exactly at 1 like Moog. Limit input gain before high resonance.

Does mutating params between calls reset state? No — mutating the same object (params.fc = newFc) preserves state. Replacing the object (params = { fc: newFc }) loses it.

Recipes

Chain filters

import { dcBlocker } from '@audio/filter'
import { moogLadder } from '@audio/filter'

let p1 = { fc: 200, fs: 44100 }
let p2 = { R: 0.995 }
for (let buf of stream) {
  dcBlocker(buf, p2)   // DC removal first
  moogLadder(buf, p1)
}

Stereo — independent state per channel

import { moogLadder } from '@audio/filter'

let pL = { fc: 1000, fs: 44100 }
let pR = { fc: 1000, fs: 44100 }
for (let [L, R] of stereoStream) {
  moogLadder(L, pL)
  moogLadder(R, pR)
}

Notch out mains hum

import { notch } from '@audio/filter'

let p = { fc: 50, Q: 30, fs: 44100 }
for (let buf of stream) notch(buf, p)   // removes 50 Hz hum, flat elsewhere

Automate cutoff without clicks

import { variableBandwidth } from '@audio/filter'

let p = { fc: 200, Q: 1.0, fs: 44100 }
for (let buf of stream) {
  p.fc = 200 + lfo() * 1800   // mutate in-place — state preserved
  variableBandwidth(buf, p)
}

Pitfalls

New params object on every call — state resets each block

// Wrong
for (let buf of stream) moogLadder(buf, { fc: 1000, fs: 44100 })

// Right — create once, reuse
let p = { fc: 1000, fs: 44100 }
for (let buf of stream) moogLadder(buf, p)

Shared params for stereo — channels corrupt each other's state

// Wrong
let p = { fc: 1000, fs: 44100 }
for (let [L, R] of stream) { moogLadder(L, p); moogLadder(R, p) }

// Right — one object per channel
let pL = { fc: 1000, fs: 44100 }, pR = { fc: 1000, fs: 44100 }
for (let [L, R] of stream) { moogLadder(L, pL); moogLadder(R, pR) }

Filtering the same buffer twice for multi-band — second band sees pre-filtered input

// Wrong
filter(buffer, { coefs: bands[0] })
filter(buffer, { coefs: bands[1] })   // input already filtered!

// Right — copy per band
let bufs = bands.map(b => { let c = Float64Array.from(buffer); filter(c, { coefs: b.coefs }); return c })

Omitting fs — silently uses 44100 Hz math on 48000 Hz audio

// Wrong — wrong cutoffs at 48 kHz
moogLadder(buffer, { fc: 1000 })

// Right
moogLadder(buffer, { fc: 1000, fs: 48000 })