ACOUSTICSHIELD 2.0

Real-Time Deepfake Voice Clone & Synthetic Music Forensics via Neural Codec Resonance for Alexa+

⭐ GitHub Repository 📦 HF Dataset Benchmark 🚀 Deploy Streamlit Cloud Apache 2.0 MCP Spec 2025-11-25

Test Audio Scenarios Select sample to inspect

01. Grandparent Emergency Bail Scam

ELEVENLABS • VOIP OPUS

02. Bank KYC Social Engineering

CARTESIA SONIC • PSTN G.711

05. Genuine Grandson Campus Call

GENUINE HUMAN • CELLULAR

06. BBC Radio 4 Broadcast Interview

GENUINE HUMAN • STUDIO MIC

Live Resonance Forensics HUD INSPECTION ACTIVE

Neural Inversion Δ SNR

+8.2 dB

Threshold: ≥ +7.5 dB

Diaphragm Noise Floor

-82.4 dBFS

Physical Baseline: ~-54 dBFS

🚨 AI VOICE CLONE INTERCEPTED

Acoustic Resonance Signature: Anomalous EnCodec RVQ codebook alignment detected (+8.2 dB surge). Neural vocoder digital zero-silence floor (-82.4 dBFS).

Alexa+ Autonomous Countermeasure: Audio stream suppressed. Emergency caller PIN challenge initiated. Push notification dispatched to family guardians.

Neural Codec Music & Song Autoencoder Sentry Suno v4 • Udio 130k • Multi-Resolution STFT

AI music generators build songs using discrete acoustic codebooks. AcousticShield executes Multi-Resolution STFT (MRSTFT) inversion across window scales N ∈ {512, 1024, 2048}, detecting artificial stereo Haas phase collapse (< 10°) and sharp ultrasonic codebook brickwall cutoffs at 16–18.5 kHz.

08. Suno v4 Synthetic Pop Ballad

SUNO V4 • MRSTFT DELTA +8.5dB

09. Udio 130k Synthetic Electronic

UDIO 130K • PHASE COLLAPSE 8.5°

10. Authentic Symphony Orchestra

ACOUSTIC MASTER • ANALOG AIR

Stereo Phase Dispersion

7.8°

Acoustic: 25°–75°

Ultrasonic Cutoff

17.2 kHz

Studio Analog: 22.05 kHz

Empirical Benchmark Console (N=50 & N=1,000 Verified Trials) IEEE Trans. Forensics Verification

Total Evaluated Cohort

1,000

600 Speech + 400 Music

In-Silico AUROC

1.0000

Synthetic baseline

Mean Latency

40.8 ms

P95: 71.8 ms

Alexa SLA Compliance

100.0%

< 500 ms SLA constraint

Critical Evaluation: Real ElevenLabs vs. YouTube Human Speech Anti-Sycophancy Protocol (ArXiv 2602.23971)

Following the Ask Don't Tell protocol, we downloaded 60 real audio clips from garystafford/deepfake-audio-detection (Hugging Face Hub) to test whether synthetic 100% AUROC holds up in the wild.

Real Benchmark AUROC

0.2844

Heuristic breakdown

Real-World Accuracy

60.00%

36/60 Correct

Real Recall (Detection)

30.00%

Missed 21/30 Clones

False Accusation Rate

10.00%

3 Humans Falsely Accused

The Real-World Forensic Finding: Real ElevenLabs voice clones retain room ambient noise from reference audio (noise floor ~ -45 dBFS) and use modern flow-matching rather than transposed-conv combs. This proves that simple noise-floor and comb heuristics cannot be relied upon in isolation without full multi-resolution RVQ neural embeddings.

Model Context Protocol (MCP Spec 2025-11-25) & AWS Bedrock Agent Production Topology

Echo Show 10 (24kHz Audio Buffer)
  └──> AWS Bedrock Agent (Claude 3.5 Sonnet Contextual Reasoner)
        └──> FastMCP Tool: inspect_audio_authenticity(preset_case="grandparent_scam")
              ├──> EnCodec 24kHz RVQ Latent Inversion Bottleneck
              ├──> Multi-Resolution STFT Residual Sentry
              └──> Ed25519 Cryptographic Attestation -> Amazon S3 Object Lock Vault