@audio/denoise-omlsa

OM-LSA: Optimally Modified Log-Spectral Amplitude (Cohen & Berdugo 2001), with IMCRA noise (Cohen 2003) or a noise learned where it plays alone

npm install @audio/denoise-omlsa

import omlsa from '@audio/denoise-omlsa'

Optimally-Modified Log-Spectral Amplitude estimator (Cohen & Berdugo 2001) with IMCRA noise estimation (Cohen 2003). The log-spectral amplitude gain when speech is present, weighed against a floor by the speech presence probability p: G = G_H1^p · G_min^(1−p) (eq. 16). The a priori SNR is decision-directed on GH1 (eq. 18), the a priori speech absence is estimated from its spread over time and neighbouring bins (§4), the noise spectrum comes from IMCRA. As Cohen's own omlsa.m, a bin whose speech absence reaches 0.9 counts as noise: noise that happens to peak keeps Gmin and leaves no musical tone, so what remains of the noise is the noise, Gmin quieter. Time constants are set for 8 ms frames and rescaled to the actual frame step (omlsa.m rescales by the reciprocal, which lengthens them at 48 kHz). scripts/reference.py holds a numpy version written from the papers; on 16 kHz VoiceBank frames it gives omlsa.m's noise track, speech absence and gains to the last bit (but for near-empty bins, where omlsa.m's absolute 1e-10 floors bind), and test.js holds this code to it.

A noise that holds still can be learned instead: profile, the noise's PSD (noiseProfile of a stretch where it plays alone), or, in the batch form, noiseFrames/profileFrom/profileTo naming that stretch. The noise is then held (known of @audio/noise-estimate), and p reads the observation alone: (9) at the a priori SNR of speech ξH1 = 15 dB and q = 0.5 (Gerkmann & Hendriks 2012), and 0 where the estimated q reaches 0.9, as before. The decision-directed ξ lags a word's onset; a known noise need not wait for it. On the VoiceBank+DEMAND test set, the noise learned from the half second before each speaker starts and Gmin −12 dB: PESQ 2.48, SI-SDR 15.2 dB, OVRL 2.89, against 2.40, 14.5 and 2.86 tracked; on steady noise under speech and music the noise goes exactly Gmin down, with no musical noise from −12 to −20 dB (log kurtosis ratio 0.00; in the half second after music stops, 0.06 at −12 dB and 0.46 at −20). processor(opts) is the gain as an @audio/stft frame process, for a host running its own frames: audio's denoise op learns a print from a range and runs it so.

omlsa(data, { fs })
omlsa(data, { fs, gMin: -10 })                                 // gentler: more room tone left
omlsa(data, { fs, profileFrom: 0, profileTo: fs / 2, gMin: -12 })   // the noise learned from the first half second

| Param | Default | | |---|---|---| | gMin | -15 | dB: what noise-only bins keep (alias gMinDb) | | alpha | 0.98 | Decision-directed smoothing (alias of alphaDD) | | xiMin | 10^−2.5 | A priori SNR floor (−25 dB) | | qPrior | estimated | A fixed a priori speech absence instead of the estimate | | frameSize | power of two nearest 32 ms | STFT frame: 512 at 16 and 22.05 kHz, 1024 at 44.1, 2048 at 48 (frame(fs)) | | hopSize | frameSize/4 | | | profile | tracked | A known noise PSD (frameSize/2+1 bins), held | | noiseFrames / profileFrom / profileTo | | A noise-only stretch to learn the profile from (batch) |

Use when: speech in non-stationary noise (street, café, car); generally the highest-quality choice for noisy speech. A steady noise with a stretch of it alone (hiss, hum, a fan, room tone): learn it, profile.

Part of @audio/denoise — the denoise family umbrella. This README is generated from the umbrella docs.

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