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

Detect and reconstruct clipped samples.

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.