Built for the witness stand. We extract critical speech from intrusive background noise using deterministic, Time-Variant Spectral Subtraction. No Generative AI. No guessing. Just reproducible, peer-reviewed digital signal processing that survives strict Daubert challenges.
| Forensic Standard | Legacy "Audio Artist" | Generative AI SaaS | MatterMath Engine |
|---|---|---|---|
| Processing Methodology | Subjective EQ adjustments | Neural network guessing | Deterministic Physics (DSP) |
| Accuracy Standard | ✕ Human Bias | ✕ Hallucinates Fake Data | ✓ Mathematically Auditable |
| Reproducibility | ✕ Subjective | ✕ Stochastic | ✓ Strictly Reproducible |
| Evidence Integrity | ⚠ Vulnerable in Court | ✕ Inadmissible Black-Box | ✓ SOC-2 Compliant Verification |
To prove our software does not destroy evidence, we provide the court with three tracks: RAW (A), CLEAN (B), and DELTA NOISE (C). The Delta Track contains the exact static removed from the file. If opposing counsel cannot hear human speech in the Delta track, they cannot claim we deleted the suspect's voice.
Acoustic Challenge: High-momentum echo, static and analog cellular dropout obscuring key dialogue during surveillance.
Acoustic Challenge: Dense, flat VHF aviation static masking low-amplitude, calm vocal transients in cockpit recordings.
Acoustic Challenge: Modern body-worn cameras suffer from intense clothing friction, unpredictable wind buffeting, and erratic kinetic movement masking suspect or witness admissions.
As machine learning and Generative AI increasingly populate the commercial audio processing market, the forensic admissibility of digital audio evidence is under unprecedented threat. Generative models operate on stochastic principles—hallucinating missing audio data to produce subjectively pleasing results—which fundamentally violates the principles of forensic evidence handling. The MatterMath Engine eschews artificial intelligence entirely. This paper outlines our non-stochastic, mathematically deterministic methodology utilizing Time-Variant Spectral Subtraction and introduces the Digital Null Test as a verifiable proof of evidentiary integrity suitable for federal and state evidentiary hearings.
Download Full Technical PDFA demonstration of how we generate Daubert-compliant evidence without the use of black-box generative AI.
Technology alone cannot defend evidence on the stand. MatterMath is supported by an advisory board of certified forensic audio examiners. If your case requires live testimony, we provide comprehensive forensic reporting and expert witness services to authenticate our digital null test in front of a judge or jury.
Upload your contested media file. Before the Daubert-compliant extraction begins, the MatterMath engine automatically secures your chain of custody and verifies the physical integrity of the recording.
Instant MD5 and SHA-256 hash generation upon upload to legally lock and verify your file's chain of custody.
Automated software scan for digital clipping, DC offset errors, and physical splice-tampering markers.