
AI audio watermark removal & forensic anti-attribution toolkit. Neutralizes Suno, Udio, and neural fingerprints with ISO 226 perceptual masking.
Defensive Psychoacoustic Audio DSP Toolkit & Forensic Anti-Attribution Engine
Surgically neutralize Suno, Udio, and synthetic audio watermarks with zero perceptible quality degradation.
Conceived, Architected, and Authored by Ahmad Hassan (B-Ted)
ποΈ Launch Web Audio Studio β’ Capability Matrix β’ 14-Vector Defense β’ Architecture β’ Research Specs β’ Governance
Generative audio models (Suno, Udio, ElevenLabs, Google SynthID) inject proprietary synthetic watermarks, high-frequency ultrasonic carriers (19.5 kHz), phase-locked spread spectrums, and latent provenance tags into generated waveforms. When musicians, producers, or sound designers incorporate AI stems, sound effects, or background textures into human compositions, their entire track becomes forensically tagged for platform surveillance and automated copyright attribution.
NaSuno is a defensive, mathematical signal processing framework designed to decouple human musical creativity from synthetic platform tracking. Built on ISO 226 equal-loudness contours, Bark-scale critical-band psychoacoustics, and zero-phase IIR notch filtering, NaSuno strips forensic tracking markers while maintaining complete spectral transparency, transient punch, and stereo correlation.
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β NASUNO CORE CAPABILITIES β
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β π¨ Watermark & Carrier Purge β Psychoacoustic Masking β Forensic Anti-Attribution β WAV β
β - Ultrasonic Notch (19.5kHz) β - ISO 226 Equal Loudness β - Suno AI Carrier Excision β - FLACβ
β - SynthID Inaudible Spread β - Bark-Scale Masking Latency β - Udio Latent Neutralization β - MP3 β
β - Zero-Phase Bi-Directional β - TPDF Dither Reconstruction β - C2PA / RIFF Tag Stripping β - OGG β
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INFO chunks, ID3 tags, and BWF acoustic metadata.