Crate cathar 

Audio restoration toolbox — denoise, de-hum, de-click, de-clip, normalise.

Default denoising uses spectral subtraction (pure Rust, zero weights). Enable the ml feature for candle-based neural denoising (Demucs, DNS Challenge).

Quick start

use cathar::{Denoiser, SpectralDenoiser, generate_wave};

let audio = generate_wave(44100, 440.0, 1.0, 0.2);
let denoiser = SpectralDenoiser::default();
let clean = denoiser.denoise(&audio)?;
assert_eq!(clean.channels[0].len(), audio.channels[0].len());

Structs

AudioData

NoisePrint

Pre-computed noise profile from a silence segment. Feed into SpectralDenoiser::with_noise_print instead of auto-detection.

SpectralDenoiser

Enums

Error

Traits

Denoiser

Functions

bandwidth_extend

Restore high-frequency content lost to compression or low sample rates.

breath_remove

Detect and attenuate breath sounds between speech segments.

declick

Detect and interpolate impulse clicks.

declip

Reconstruct clipped samples with least-squares AR interpolation (LSAR).

deesser

Reduce sibilance (harsh “s”, “sh”, “ch” sounds) using HF compression.

dehum

Remove mains hum (50/60 Hz + harmonics) using cascaded notch filters.

dereverb

Remove room reverb using spectral envelope decay gating.

generate_wave

learn_noise_print

Learn a noise profile from an audio segment (should be silence/noise-only).

normalize_loudness

Scale to target integrated loudness (EBU R128 approximation via RMS). Target in LUFS: -23 = broadcast, -16 = podcast, -14 = streaming.

normalize_peak

Scale to target peak level in dBFS (0 dBFS = ±1.0, -3 dBFS = ~±0.707).

variance

voice_isolate

Isolate speech from background using energy-based VAD + spectral gating.

wiener_denoise

Wiener-filter denoiser — statistically optimal, better transients.