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Iv.","email":"dfcreative@gmail.com"},"license":"MIT","homepage":"https://github.com/audiojs/denoise/tree/main/packages/denoise-omlsa","keywords":["audio","dsp","denoise","restoration","omlsa"],"repository":{"url":"git+https://github.com/audiojs/denoise.git","type":"git","directory":"packages/denoise-omlsa"},"description":"OM-LSA: Optimally Modified Log-Spectral Amplitude (Cohen & Berdugo 2001), with IMCRA noise (Cohen 2003) or a noise learned where it plays alone","maintainers":[{"name":"jamen","email":"jamenmarz@gmail.com"},{"name":"dfcreative","email":"df.creative@gmail.com"},{"name":"dy","email":"df.creative@gmail.com"}],"readme":"# @audio/denoise-omlsa [![npm](https://img.shields.io/npm/v/@audio/denoise-omlsa)](https://www.npmjs.com/package/@audio/denoise-omlsa) [![MIT](https://img.shields.io/badge/MIT-%E0%A5%90-white)](https://github.com/krishnized/license)\n\nOM-LSA: Optimally Modified Log-Spectral Amplitude (Cohen & Berdugo 2001), with IMCRA noise (Cohen 2003) or a noise learned where it plays alone\n\n```\nnpm install @audio/denoise-omlsa\n```\n\n```js\nimport omlsa from '@audio/denoise-omlsa'\n```\n\nOptimally-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 = max(G_H1, G_min)^p · G_min^(1−p)` (eq. 16 with G<sub>H1</sub> floored at G<sub>min</sub>: as written it took a bin below G<sub>min</sub> wherever G<sub>H1</sub> was under it, to −18 dB at G<sub>min</sub> −12, so `gMin` was not the floor). The a priori SNR is decision-directed on G<sub>H1</sub> (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 G<sub>min</sub> and leaves no musical tone, so what remains of the noise is the noise, G<sub>min</sub> quieter. Time constants, the decision-directed α among them, 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).\n\nα sets how soon ξ follows a word's start after a pause, and how much gain a noise peak gets. Until 0.3 it was 0.98 per frame, whatever the frame (0.985 per 8 ms at 48 kHz, 0.972 at 44.1), and a word's second and third frames after a pause lost 10 and 5 dB. It is now the lowest value per 8 ms that leaves steady white and pink noise without musical noise: 0.97 tracking the noise (log kurtosis ratio 0.00 at G<sub>min</sub> −15 and −25 dB; 0.96 left 1.16 and 0.49 at −25, the paper's 0.92 0.99 and 1.81 at −15), 0.95 on a learned noise (`omlsa.m`'s; 0.92 left 0.19 and 0.42). The paper's cap q ≤ q<sub>max</sub> = 0.95 (Table 1) in place of the gate kept a word's second frame after a pause another 3 dB but left musical noise on steady noise (0.24 and 0.46 at α 0.97), so the gate stays. The table under [Speech](https://github.com/audiojs/denoise#speech) has what each keeps. `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.\n\nA 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 ξ<sub>H1</sub> = 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 G<sub>min</sub> −12 dB: PESQ 2.45, STOI 0.919, SI-SDR 15.1 dB, OVRL 2.88 (0.2: 2.48, 0.915, 15.2, 2.89), against 2.36, 0.919, 14.0 and 2.84 tracked; on steady noise under speech and music the noise goes exactly G<sub>min</sub> down, with no musical noise from −12 to −20 dB (log kurtosis ratio 0.00) but for the half second after music stops (0.11 at −12 dB and 1.00 at −20, `scripts/broadband.mjs`). `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.\n\n```js\nomlsa(data, { fs })\nomlsa(data, { fs, gMin: -10 })                                 // gentler: more room tone left\nomlsa(data, { fs, profileFrom: 0, profileTo: fs / 2, gMin: -12 })   // the noise learned from the first half second\n```\n\n| Param | Default | |\n|---|---|---|\n| `gMin` | `-15` | dB: what noise-only bins keep, the floor (alias `gMinDb`) |\n| `alpha` | `0.97`; `0.95` with a `profile` | Decision-directed smoothing per 8 ms of frame step, rescaled to the actual step (alias of `alphaDD`) |\n| `xiMin` | `10^−2.5` | A priori SNR floor (−25 dB) |\n| `qPrior` | estimated | A fixed a priori speech absence instead of the estimate |\n| `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)`) |\n| `hopSize` | `frameSize/4` | |\n| `profile` | tracked | A known noise PSD (`frameSize/2+1` bins), held |\n| `noiseFrames` / `profileFrom` / `profileTo` | | A noise-only stretch to learn the profile from (batch) |\n\n**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`.\n\n---\n\nPart of [@audio/denoise](https://github.com/audiojs/denoise) — the denoise family umbrella. This README is generated from the umbrella docs.\n\nMIT © [audiojs](https://github.com/audiojs)\n","readmeFilename":"README.md"}