@audio/denoise

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):

1. tonal hum (Goertzel — ≥2 of first 3 harmonics show 50× line/off-line ratio at 50 or 60 Hz)
2. impulses (excess kurtosis of AR residual > 12)
3. sibilance (high/mid band power ratio > 8)
4. LF rumble (low/mid band power ratio > 3)
5. non-stationary noise (CV of the rolling-minimum frame-energy floor > 0.3) → omlsa
6. 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