orbit-forensics 1.1.1

High-fidelity AI audio forensics and spectral anomaly detection

@ohnrshyp/forensics

High-fidelity audio signal forensics and tampering detection library.

This module runs a deep, signal-level acoustic forensics suite to detect structural manipulations, lossy audio transcoding, synthetic phase alignments, and periodic upsampling artifacts common in AI-generated audio (such as neural vocoders and synthesis generators).

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🚀 Key Forensic Diagnostics

• 📐 Phase Entropy (Instantaneous Group Delay): Estimates the Shannon phase entropy of the signal to catch artificial vocoding, pitch correction (autotune), or synthetic phase shifts.
• 📉 Spectral Cutoff Check: Detects brick-wall frequency rolloff cutoffs (e.g. at 16kHz or 20kHz) indicating low-bitrate MP3/AAC compression transcodes or training-data restrictions.
• 🧩 Upsampling/Checkerboard Artifact Detector: Captures periodic cepstral peak ratios associated with checkerboard spectral upsampling artifacts left behind by generative networks.
• 🔀 Stereo Mid/Side (M/S) Coherence: Analyzes stereo channel phase and energy distributions to flag artificial stereo widening or phase cancellations.
• ⚡ Pre-echo Transient Check: Analyzes onset transients to expose temporal smearing and pre-echo artifacts typical of frame-based audio codecs.
• 🎹 Pitch Jitter & Modulation (Vibrato Jitter): Scans pitch contours to flag perfectly linear pitch modulations indicating synthesized vocoder vibratos.
• 📜 Chroma & Flux Variance: Measures timbral/spectral evolution variance (flux, centroid, zero-crossing rate) to flag abnormally static synthesis.

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🔬 Architectural & Mathematical Design

The library couples a Node.js child process connector with a scientific python script leveraging Librosa, NumPy, and SciPy.

1. Phase Entropy

Instantaneous group delay describes the derivative of the phase spectrum along the frequency axis. A Short-Time Fourier Transform (STFT) yields complex matrix $D(f, t)$: $$\text{Phase}(f, t) = \angle D(f, t)$$ $$\text{Instantaneous Frequency}(f, t) = \text{Phase}(f, t) - \text{Phase}(f, t-1)$$ For a series of frequency bins, the histogram of instantaneous frequency changes is computed. Shannon entropy is calculated over the histogram probabilities $p_i$: $$H = -\sum_{i} p_i \log_2(p_i)$$ Natural audio yields high entropy ($H \ge 4.5$) due to complex harmonic variance. Artificial alignment or vocoder synthesis yields highly structured phase sequences, leading to abnormally low entropy ($H < 3.5$).

2. Cepstral Checkerboard peak ratio

To detect upsampling artifacts common in neural vocoders, the mean log-magnitude spectrum is computed: $$\bar{S}(f) = \frac{1}{T} \sum_{t} \log |D(f, t)|$$ The real cepstrum is calculated by taking the inverse FFT of the log spectrum: $$\text{Cepstrum} = \text{Real}(\text{IFFT}(\bar{S}(f)))$$ Periodic upsampling artifacts create distinct peaks in the high-quefrency region of the cepstrum. The ratio of the maximum peak amplitude to the average cepstral envelope amplitude exposes these vocoder structures.

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📦 Installation

Node.js (NPM Package)

npm install @ohnrshyp/forensics

Python (PyPI Package)

pip install orbit-forensics

Host Dependencies

This package delegates spectral processing to Python. Ensure Python 3.8+ is installed on the host along with:

pip install librosa numpy scipy

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🛠️ Node.js API Reference

analyze(input, [options])

Performs deep spectral forensics checks.

• Parameters:

  • input (Buffer | string): Raw binary buffer or absolute path to the target audio file.
  • options (Object, optional):
    • maxLength (number): Limit analysis to the first $N$ seconds of the file. Default is 120.
    • stemsDir (string | null): Optional path to a directory containing separated stems (e.g. Demucs vocal/bass/other stems) to perform advanced stem-aware forensics (e.g., vocal-specific cutoff, instrumental bleed check).
    • verbose (boolean): Enable diagnostic logging. Default is false.

• Returns: Promise<Object> containing the following schema:

  {
    "spectral_cutoff": {
      "available": true,
      "has_16k_cutoff": false,
      "energy_ratio_above_16k": 0.0412,
      "energy_below_16k": 12.421,
      "energy_16k_to_20k": 0.512
    },
    "phase_entropy": {
      "mean_entropy": 4.892,
      "std_entropy": 0.241,
      "normalized_entropy": 0.815,
      "low_entropy": false
    },
    "checkerboard": {
      "available": true,
      "cepstral_peak_ratio": 3.412,
      "has_artifacts": false
    },
    "pre_echo": {
      "available": true,
      "mean_pre_echo_ratio": 0.081,
      "has_pre_echo": false
    },
    "ms_phase_coherence": {
      "available": true,
      "sub_bass_sm_ratio": 0.091,
      "low_mid_sm_ratio": 0.241,
      "ms_anomalous": false
    },
    "pitch_jitter": {
      "available": true,
      "perfect_vibrato": false
    },
    "processingTimeMs": 1420
  }
  

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📊 Diagnostic Interpretation Matrix

Combine these metrics to diagnose the structural state of your audio:

Diagnostic Metric	Pristine Master	Lossy Transcode (MP3/AAC)	AI-Generated (Vocoder/Synthesis)
Phase Entropy (normalized_entropy)	High ($\ge 0.75$)	Moderate ($0.65 - 0.75$)	Low ($< 0.55$)
Spectral Cutoff (has_16k_cutoff)	false	true (if transcode is $< 192$ kbps)	true (if trained on MP3 datasets)
Checkerboard Artifacts (has_artifacts)	false	false	true (periodic cepstral peak)
Pre-echo (has_pre_echo)	false	true (temporal framing artifacts)	false / true (varies by vocoder)
M/S Stereo Coherence (ms_anomalous)	false	false	true (artificial spatialization smearing)

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💻 Code Examples

Analyzing audio for AI generation or compression anomalies

const forensics = require('@ohnrshyp/forensics');
const fs = require('fs');

async function verifyAudio() {
  const audioBuffer = fs.readFileSync('uploaded-track.wav');

  try {
    const report = await forensics.analyze(audioBuffer, {
      maxLength: 60, // inspect first 60 seconds
      verbose: true
    });

    console.log('--- Forensic Report ---');
    console.log(`Phase Entropy: ${report.phase_entropy.mean_entropy} (Low: ${report.phase_entropy.low_entropy})`);
    console.log(`Brickwall 16kHz Cutoff: ${report.spectral_cutoff.has_16k_cutoff}`);
    console.log(`Neural Vocoder Upsampling Peak: ${report.checkerboard.has_artifacts}`);
    console.log(`Stereo Phase Anomaly: ${report.ms_phase_coherence.ms_anomalous}`);

    if (report.phase_entropy.low_entropy && report.checkerboard.has_artifacts) {
      console.warn('⚠️ WARNING: Audio exhibits characteristics of neural synthesis/AI vocoding!');
    } else if (report.spectral_cutoff.has_16k_cutoff) {
      console.warn('⚠️ WARNING: Audio has been heavily compressed or transcoded from an MP3 source.');
    } else {
      console.log('✅ PASS: Audio file signal matches a pristine original master.');
    }

  } catch (error) {
    console.error('Forensics check failed:', error.message);
  }
}

verifyAudio();

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📄 License

Licensed under the Apache License, Version 2.0 (the "License"). See LICENSE in the project root for details.