Phase I · Institutional Research Tier

Wavelet scattering, shipped as a wheel.

Drop-in C++/CUDA primitives for any audio, vibration, or biosignal pipeline that needs translation-invariant, frequency-aware fingerprints. AGPL-3.0 + Commercial.

WSTConfig

J = 8

Q = 16

Depth

JTFS

16,512 scattering paths
at depth 2

Time domain: λ=0

Frequency domain: bank

Filter λ=0 · ξ=π·2^(-0/16) · σ=0.800 samples · peak=0.051 (analytic Morlet, normalised)

What you get

→omni-wst-core PyPI wheel: WSTEngine<HopperTag, J, Q> + analytic Morlet bank + Radix-2 FFT CPU fallback.
→JTFS (joint time-frequency scattering) on dual CUDA streams.
→15+ GB/s DMA via cudaHostRegister on pinned Plasma pages.

What it solves

Spectrogram-and-CNN pipelines burn enormous compute to learn what scattering provides analytically.
WST gives you orderable, comparable fingerprints with formal stability guarantees, in milliseconds.

Pricing

AGPL-3.0: free for research & open source

Enterprise use cases

Audio QA / royalty enforcement

Detect derivative tracks in a 100M-asset catalogue using deterministic fingerprints.

Bioacoustic monitoring

Species and event classification on field recordings without retraining for each habitat.

Industrial vibration analytics

Spot the fault signature in rotating machinery before a model would even converge.

Quickstart

from omni_wst_core import fingerprint, WSTConfig
fp = fingerprint(signal, WSTConfig(J=8, Q=16, depth=2, jtfs=False))

↗ View on PyPI Star omni-wst-core on GitHub ↗Talk to founders →