@audio/denoise [](https://www.npmjs.com/package/@audio/denoise) [](LICENSE)

July 10, 2026 · View on GitHub

Single-pass noise reduction. 13 specialised methods + an auto-classifier.

	Domain	Targets	Quality	CPU	Best for
denoise	meta	auto	—	varies	"just clean it"
gate	time	silence	★	very low	hard cut at threshold
dehum	time	mains hum	★★★★	very low	50/60 Hz + harmonics
specsub	freq	broadband stationary	★★	medium	baseline
wiener	freq	broadband stationary	★★★	medium	general broadband
omlsa	freq	broadband non-stationary	★★★★	high	speech in changing noise
declick	time	impulses	★★★★	medium	vinyl pops, edit clicks
decrackle	time	dense impulses	★★★	medium	shellac crackle
declip	time	hard clipping	★★★	medium	restoration
dewind	time	LF rumble	★★★	very low	wind, handling noise
deplosive	time	LF bursts	★★★	low	mic plosives (p, b)
deesser	time	sibilance	★★★★	low	voice (s, sh)
debreath	time	inter-word noise	★★★	low	breath / hiss in pauses
dereverb	freq	late reverb	★★	medium	moderate room reverb

For broader DSP needs use stretch, shift, pitch, beat.

Usage

npm install @audio/denoise

import { denoise, dehum, wiener, declick } from '@audio/denoise'

let cleaned   = denoise(samples)                              // auto-classify + dispatch
let unhummed  = dehum(samples, { freq: 60 })                  // explicit method
let { out, plan } = denoise(samples, { returnPlan: true })    // see what was chosen

// Streaming — pass options first, then call repeatedly with chunks.
let write = wiener({ fs: 48000 })
write(block1)
write(block2)
write()                                                        // → flush remaining samples

Mono Float32Array in/out. State lives on the params object; pass the same one across calls and biquad memory / spectral history persists. For stereo, process channels independently.

`denoise`

Content-aware auto-selector. Runs a single STFT classification sweep over the input and dispatches to the most suitable method.

denoise(data)                                                  // → cleaned Float32Array
denoise(data, { returnPlan: true })                            // → { out, plan }
denoise(data, { force: 'wiener' })                             // skip classifier

Param	Default
`fs`	`44100`	Sample rate
`force`	—	One of `'dehum' \| 'declick' \| 'dewind' \| 'deesser' \| 'dereverb' \| 'omlsa' \| 'wiener'`
`returnPlan`	`false`	Return `{ out, plan }` with classifier scores + chosen method

Routing (in priority order):

tonal hum (Goertzel — ≥2 of first 3 harmonics show 50× line/off-line ratio at 50 or 60 Hz)
impulses (excess kurtosis of AR residual > 12)
sibilance (high/mid band power ratio > 8)
LF rumble (low/mid band power ratio > 3)
non-stationary noise (CV of the rolling-minimum frame-energy floor > 0.3) → omlsa
otherwise → wiener

Tonal & narrowband

`dehum`

Cascade of high-Q biquad notches at the fundamental + harmonics.

dehum(data, { freq: 60, harmonics: 4 })
dehum(data, { freq: 50, adaptive: true, drift: 0.5 })          // tracks slow mains drift

Param	Default
`freq`	`50`	Fundamental (Hz)
`harmonics`	`4`	Number of notches placed
`Q`	`30`	Notch sharpness — higher = narrower
`adaptive`	`false`	Goertzel sweep refines `freq` ± `drift` Hz

Use when: mains buzz, ground-loop hum, fixed tonal interference.
Not for: broadband noise (use wiener/omlsa); shifting tones (use spectral methods).

`dewind`

Adaptive high-pass. Cutoff slides between cutoffMin and cutoffMax based on the LF/MF energy ratio.

dewind(data, { cutoffMin: 60, cutoffMax: 250 })

Param	Default
`cutoffMin`	`60`	Hz — minimum cutoff (LF mostly clean)
`cutoffMax`	`250`	Hz — maximum cutoff (heavy rumble)
`order`	`2`	HP sections (each 12 dB/oct)
`Q`	`0.707`	Butterworth-ish
`blockSize`	`1024`	Coefficient update interval (samples)

Use when: intermittent wind buffeting, handling thumps, low-frequency room modes — the adaptive cutoff opens on gusts and closes between them (measured: beats wiener on gusty wind at ~1/10 the CPU).
Not for: continuous rumble under speech — a time-domain cutoff can't separate overlapping spectra; use wiener/omlsa there (measured ~9 dB vs ~1 dB SNR gain). An LPC-null post-filter was evaluated and rejected: voiced speech is as AR-predictable as wind, so nulling wind poles whitens vowels too (LSD improves, SNR and speech level degrade).

`deplosive`

Splits the signal into an LF band (< crossover) and its exact complement; ducks the LF band when its energy spikes above triggerRatio× the high band (a plosive signature). With no plosive present the output equals the input sample-for-sample — no crossover coloration.

deplosive(data, { triggerRatio: 4, attack: 0.005, release: 0.08 })

Param	Default
`triggerRatio`	`4`	LF/high energy ratio that opens the duck
`attenuation`	`-18`	dB cut on the LF band when triggered
`crossover`	`200`	Hz — LF/high split point
`attack`	`0.005`	s
`release`	`0.08`	s

Use when: mic plosives (p, b, t) producing low-frequency thuds.

`deesser`

Dynamic peaking EQ centred on the sibilance band. Detection runs on a HP side-chain; when the envelope exceeds threshold, a negative-gain peaking EQ at freq engages on the audio path. Re-computed every block samples for smooth gain riding. Backed by @audio/dynamics-deesser mode: 'band' since the 2026-07 near-dupe merge — same seconds-based options here.

deesser(data, { freq: 6500, threshold: -28, ratio: 4 })

Param	Default
`freq`	`6000`	Sibilance centre (Hz)
`threshold`	`-30`	dBFS — engagement level
`ratio`	`4`	Compression ratio above threshold
`attack`	`0.001`	s — how fast the cut engages
`release`	`0.05`	s — how slowly it recovers
`Q`	`1.4`	Peaking EQ Q
`block`	`64`	Coefficient update interval (samples)

Use when: voice post-production with hot s/sh; vocal bus de-essing.

Broadband & spectral

`specsub`

Berouti spectral subtraction (1979). Estimates noise from the first noiseFrames (or tracks it via Minimum Statistics) and subtracts α(γ)·N̂(k) — an SNR-adaptive over-subtraction factor — from each magnitude frame, with a β·|Y(k)|² spectral floor. Pass an explicit alpha to force a fixed factor.

specsub(data, { beta: 0.02, noiseFrames: 6 })                 // adaptive α(γ)
specsub(data, { alpha: 2, beta: 0.02 })                       // fixed over-subtraction

Param	Default
`alpha`	adaptive	Fixed over-subtraction factor; omit for Berouti α(γ)
`beta`	`0.02`	Spectral floor (fraction of the noisy spectrum)
`noiseFrames`	first 4 frames	Leading noise-only frames for the PSD bootstrap

Use when: quick baseline; offline cleanup with a known noise-only preamble.
Not for: musical-noise-sensitive material — use wiener or omlsa.

`wiener`

MMSE Wiener / log-MMSE (Ephraim-Malah 1984/1985) with decision-directed a-priori SNR.

wiener(data, { rule: 'wiener' })                              // pure Wiener gain
wiener(data)                                                   // defaults: 'mmse-lsa' rule

Param	Default
`rule`	`'mmse-lsa'`	`'wiener'` or `'mmse-lsa'` (log-spectral, less musical noise)
`alpha`	`0.98`	Decision-directed smoothing (alias of `alphaDD`)
`frameSize`	`2048`	STFT frame
`hopSize`	`frameSize/4`	OLA hop
`noiseFrames`	first 4 frames	Leading noise-only frames for PSD bootstrap

Use when: transparent broadband denoise; the "safe default" for stationary noise.

`omlsa`

Optimally-Modified Log-Spectral Amplitude estimator (Cohen 2002) driven by IMCRA noise tracking. Combines an LSA gain with a minimum-gain floor weighted by speech presence probability: G = G_LSA^p · G_min^(1-p).

omlsa(data)
omlsa(data, { gMinDb: -25 })                                   // less aggressive floor

Param	Default
`gMinDb`	`-20`	dB floor for non-speech bins (alias of `gMin`)
`alpha`	`0.92`	Decision-directed smoothing (alias of `alphaDD`)
`frameSize`	`2048`
`hopSize`	`frameSize/4`

Use when: speech in non-stationary noise (street, café, car); generally the highest-quality choice for noisy speech.

Impulses

`declick`

Detects impulses as AR-residual outliers (> threshold·σ); replaces each click region with an AR-LS interpolation (Janssen 1986 / Godsill-Rayner 1998).

declick(data, { threshold: 4, order: 60 })

Param	Default
`threshold`	`4`	σ-multiple for click detection
`order`	`60`	AR model order
`guard`	`2`	Extra samples on each side of the detected click
`maxBurst`	`64`	Longest run repaired (longer → left as a real transient)

Use when: vinyl pops, edit clicks, occasional impulse noise.
Not for: dense crackle (use decrackle); long dropouts (use arInterpolate directly).

`decrackle`

Continuous AR-residual outlier detection with MAD-based threshold. Suited to high-rate impulse noise.

decrackle(data, { threshold: 3 })

Use when: shellac / 78 RPM crackle; persistent low-amplitude clicks.

`declip`

Detects runs of samples at ±clipLevel, fits AR on the un-clipped neighbourhood, extrapolates a sign-constrained interpolation.

declip(data, { clipLevel: 0.95 })                              // explicit threshold
declip(data)                                                   // auto-detects clip level

Param	Default
`clipLevel`	auto	Detected from histogram of \|x\| > 0.5
`order`	`100`	AR model order
`maxRun`	`order/2`	Longest run that gets restored

Use when: hard digital clipping with short clip runs.
Not for: sustained clipping covering many cycles (use sparsity-based methods).

Reverb

`dereverb`

Late-reverb suppression (Lebart, Boucher & Denbigh 2001 estimate, Habets-class gain). Models the late tail as a decaying sum of past frames' power, then applies a decision-directed Wiener gain on the signal-to-reverb ratio — the cross-frame smoothing suppresses the musical noise hard subtraction produces.

dereverb(data, { t60: 0.6, predelay: 0.04 })

Param	Default
`t60`	`0.5`	Assumed reverberation time (s)
`predelay`	`0.04`	Direct-sound passthrough (s)
`alpha`	`1.5`	Reverb-PSD over-estimation factor
`alphaDD`	`0.98`	Decision-directed SIR smoothing
`gMin`	`0.05`	Gain floor for reverb-dominated bins

Use when: moderate room reverb (RT60 ≤ 1 s) on a single channel.
Not for: heavy reverb or convolutive distortion — use multi-channel WPE (out of scope).

Gates & inter-word

`gate`

Look-ahead noise gate with hysteresis. Backed by @audio/dynamics-gate since the 2026-07 near-dupe merge — same seconds-based options here; closeThreshold (default threshold − 6 dB) sets the hysteresis close level.

gate(data, { threshold: -45, attack: 0.005, release: 0.1, hold: 0.05, lookahead: 0.005 })

Use when: silence enforcement; aggressive cut between phrases.
Not for: continuous denoise — use wiener/omlsa.

`debreath`

VAD-driven inverse gate. Uses energy + spectral flatness with a percentile-based noise floor; attenuates frames classified as non-speech with smooth attack/release.

debreath(data, { range: -10 })                                // -10 dB on non-speech (default -12)

Use when: breath, mouth noise, hiss in pauses on a voiceover.

Quality measurement

import { snr, segSnr, lsd, nrr, speechAttenuation } from '@audio/denoise'

snr(reference, processed)                                       // global SNR (dB)
segSnr(reference, processed)                                    // segmental SNR (dB)
lsd(reference, processed)                                       // log-spectral distance
nrr(noisyInput, processed)                                      // noise reduction ratio
speechAttenuation(reference, processed)                         // dB lost on speech segments

Metric	Higher is better	What it captures
`snr`	✓	Energy ratio reference / error
`segSnr`	✓	Time-localised SNR — better correlates with perception
`lsd`	✗	Mean log-magnitude error per bin
`nrr`	✓	Floor reduction in non-speech regions
`speechAttenuation`	✗	Loss of speech energy (over-aggressive denoising)

Lower-level building blocks

import { stftBatch, stftStream, stftAnalyse } from '@audio/denoise'
import { vad, spp, ddSnr } from '@audio/denoise'
import { noiseProfile, minStats, imcra } from '@audio/denoise'

stft* — analysis-modification-synthesis with Hann + ∑win² OLA reconstruction. Visit (mag, phase, state, ctx) => { mag, phase }.
vad — frame-level activity (energy + spectral flatness, percentile floor).
spp — per-bin Speech Presence Probability under Gaussian model.
ddSnr — decision-directed a-priori SNR (Ephraim-Malah).
noiseProfile — average PSD over leading frames.
minStats — Martin (2001) minimum-statistics noise PSD tracker.
imcra — Cohen (2003) Improved Minima-Controlled Recursive Averaging — drives omlsa.

Measurements

npm run measure produces a Markdown table of SNR / segSNR / LSD / NRR per method on canonical scenarios. Headline numbers on the included audio-lena fixture (8 s mono speech, 44.1 kHz):

scenario	SNR-in	best method	SNR-out	NRR	ms
60 Hz hum + harmonics	-5.2 dB	`dehum`	15.0 dB	6.3 dB	5
white noise (~13 dB SNR)	13.2 dB	`wiener`	19.8 dB	0.3 dB	82
clicks (vinyl-style)	24.1 dB	`declick`	44.1 dB	—	462
7 kHz sibilance	2.0 dB	`deesser`	9.5 dB	1.9 dB	5

Higher = better.

Demo

demo.html is a self-contained browser demo: pick a noise scenario, pick a method (or auto), inspect input/output waveforms, hear the difference, and read the live classifier scores.

References

Boll, Suppression of Acoustic Noise in Speech Using Spectral Subtraction, IEEE TASSP 1979.
Berouti, Schwartz, Makhoul, Enhancement of Speech Corrupted by Acoustic Noise, ICASSP 1979.
Ephraim & Malah, Speech Enhancement Using a Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator, IEEE TASSP 1984.
Ephraim & Malah, Speech Enhancement Using a Minimum Mean-Square Error Log-Spectral Amplitude Estimator, IEEE TASSP 1985.
Martin, Noise Power Spectral Density Estimation Based on Optimal Smoothing and Minimum Statistics, IEEE TSAP 2001.
Cohen, Optimal Speech Enhancement Under Signal Presence Uncertainty Using Log-Spectral Amplitude Estimator, IEEE SPL 2002.
Cohen, Noise Spectrum Estimation in Adverse Environments: Improved Minima Controlled Recursive Averaging, IEEE TSAP 2003.
Janssen, Veldhuis & Vries, Adaptive Interpolation of Discrete-Time Signals That Can Be Modeled as Autoregressive Processes, IEEE TASSP 1986.
Godsill & Rayner, Digital Audio Restoration, Springer 1998.
Lebart, Boucher & Denbigh, A New Method Based on Spectral Subtraction for Speech Dereverberation, Acta Acustica 2001.
RBJ Audio EQ Cookbook (biquad coefficients).

License

MIT