Forensic Audio Research Audio Voice Recovery Best Practices Comprehensive audio forensics and voice recovery guide providing CSI-level capabilities for recovering voice from low-quality, low-volume, or damaged audio recordings. Contains 45 rules across 8 categories, prioritized by impact to guide audio enhancement, forensic analysis, and transcription workflows. When to Apply Reference these guidelines when: Recovering voice from noisy or low-quality recordings Enhancing audio for transcription or legal evidence Performing forensic audio authentication Analyzing recordings for tampering or splices Building automated audio processing pipelines Transcribing difficult or degraded speech Rule Categories by Priority Priority Category Impact Prefix Rules 1 Signal Preservation & Analysis CRITICAL signal- 5 2 Noise Profiling & Estimation CRITICAL noise- 5 3 Spectral Processing HIGH spectral- 6 4 Voice Isolation & Enhancement HIGH voice- 7 5 Temporal Processing MEDIUM-HIGH temporal- 5 6 Transcription & Recognition MEDIUM transcribe- 5 7 Forensic Authentication MEDIUM forensic- 5 8 Tool Integration & Automation LOW-MEDIUM tool- 7 Quick Reference 1. Signal Preservation & Analysis (CRITICAL) signal-preserve-original - Never modify original recording signal-lossless-format - Use lossless formats for processing signal-sample-rate - Preserve native sample rate signal-bit-depth - Use maximum bit depth for processing signal-analyze-first - Analyze before processing 2. Noise Profiling & Estimation (CRITICAL) noise-profile-silence - Extract noise profile from silent segments noise-identify-type - Identify noise type before reduction noise-adaptive-estimation - Use adaptive estimation for non-stationary noise noise-snr-assessment - Measure SNR before and after noise-avoid-overprocessing - Avoid over-processing and musical artifacts 3. Spectral Processing (HIGH) spectral-subtraction - Apply spectral subtraction for stationary noise spectral-wiener-filter - Use Wiener filter for optimal noise estimation spectral-notch-filter - Apply notch filters for tonal interference spectral-band-limiting - Apply frequency band limiting for speech spectral-equalization - Use forensic equalization to restore intelligibility spectral-declip - Repair clipped audio before other processing 4. Voice Isolation & Enhancement (HIGH) voice-rnnoise - Use RNNoise for real-time ML denoising voice-dialogue-isolate - Use source separation for complex backgrounds voice-formant-preserve - Preserve formants during pitch manipulation voice-dereverb - Apply dereverberation for room echo voice-enhance-speech - Use AI speech enhancement services for quick results voice-vad-segment - Use VAD for targeted processing voice-frequency-boost - Boost frequency regions for specific phonemes 5. Temporal Processing (MEDIUM-HIGH) temporal-dynamic-range - Use dynamic range compression for level consistency temporal-noise-gate - Apply noise gate to silence non-speech segments temporal-time-stretch - Use time stretching for intelligibility temporal-transient-repair - Repair transient damage (clicks, pops, dropouts) temporal-silence-trim - Trim silence and normalize before export 6. Transcription & Recognition (MEDIUM) transcribe-whisper - Use Whisper for noise-robust transcription transcribe-multipass - Use multi-pass transcription for difficult audio transcribe-segment - Segment audio for targeted transcription transcribe-confidence - Track confidence scores for uncertain words transcribe-hallucination - Detect and filter ASR hallucinations 7. Forensic Authentication (MEDIUM) forensic-enf-analysis - Use ENF analysis for timestamp verification forensic-metadata - Extract and verify audio metadata forensic-tampering - Detect audio tampering and splices forensic-chain-custody - Document chain of custody for evidence forensic-speaker-id - Extract speaker characteristics for identification 8. Tool Integration & Automation (LOW-MEDIUM) tool-ffmpeg-essentials - Master essential FFmpeg audio commands tool-sox-commands - Use SoX for advanced audio manipulation tool-python-pipeline - Build Python audio processing pipelines tool-audacity-workflow - Use Audacity for visual analysis and manual editing tool-install-guide - Install audio forensic toolchain tool-batch-automation - Automate batch processing workflows tool-quality-assessment - Measure audio quality metrics Essential Tools Tool Purpose Install FFmpeg Format conversion, filtering brew install ffmpeg SoX Noise profiling, effects brew install sox Whisper Speech transcription pip install openai-whisper librosa Python audio analysis pip install librosa noisereduce ML noise reduction pip install noisereduce Audacity Visual editing brew install audacity Workflow Scripts (Recommended) Use the bundled scripts to generate objective baselines, create a workflow plan, and verify results. scripts/preflight_audio.py - Generate a forensic preflight report (JSON or Markdown). scripts/plan_from_preflight.py - Create a workflow plan template from the preflight report. scripts/compare_audio.py - Compare objective metrics between baseline and processed audio. Example usage:

1) Analyze and capture baseline metrics

python3 skills/.experimental/audio-voice-recovery/scripts/preflight_audio.py evidence.wav --out preflight.json

2) Generate a workflow plan template

python3 skills/.experimental/audio-voice-recovery/scripts/plan_from_preflight.py --preflight preflight.json --out plan.md

3) Compare baseline vs processed metrics

python3 skills/.experimental/audio-voice-recovery/scripts/compare_audio.py \ --before evidence.wav \ --after enhanced.wav \ --format md \ --out comparison.md Forensic Preflight Workflow (Do This Before Any Changes) Align preflight with SWGDE Best Practices for the Enhancement of Digital Audio (20-a-001) and SWGDE Best Practices for Forensic Audio (08-a-001). Establish an objective baseline state and plan the workflow so processing does not introduce clipping, artifacts, or false "done" confidence. Use scripts/preflight_audio.py to capture baseline metrics and preserve the report with the case file. Capture and record before processing: Record evidence identity and integrity: path, filename, file size, SHA-256 checksum, source, format/container, codec Record signal integrity: sample rate, bit depth, channels, duration Measure baseline loudness and levels: LUFS/LKFS, true peak, peak, RMS, dynamic range, DC offset Detect clipping and document clipped-sample percentage, peak headroom, exact time ranges Identify noise profile: stationary vs non-stationary, dominant noise bands, SNR estimate Locate the region of interest (ROI) and document time ranges and changes over time Inspect spectral content and estimate speech-band energy and intelligibility risk Scan for temporal defects: dropouts, discontinuities, splices, drift Evaluate channel correlation and phase anomalies (if stereo) Extract and preserve metadata: timestamps, device/model tags, embedded notes Procedure: Prepare a forensic working copy, verify hashes, and preserve the original untouched. Locate ROI and target signal; document exact time ranges and changes across the recording. Assess challenges to intelligibility and signal quality; map challenges to mitigation strategies. Identify required processing and plan a workflow order that avoids unwanted artifacts. Generate a plan draft with scripts/plan_from_preflight.py and complete it with case-specific decisions. Measure baseline loudness and true peak per ITU-R BS.1770 / EBU R 128 and record peak/RMS/DC offset. Detect clipping and dropouts; if clipping is present, declip first or pause and document limitations. Inspect spectral content and noise type; collect representative noise profile segments and estimate SNR. If stereo, evaluate channel correlation and phase; document anomalies. Create a baseline listening log (multiple devices) and define success criteria for intelligibility and listenability. Failure-pattern guardrails: Do not process until every preflight field is captured. Document every process, setting, software version, and time segment to enable repeatability. Compare each processed output to the unprocessed input and assess progress toward intelligibility and listenability. Avoid over-processing; review removed signal (filter residue) to avoid removing target signal components. Keep intermediate files uncompressed and preserve sample rate/bit depth when moving between tools. Perform a final review against the original; if unsatisfactory, revise or stop and report limitations. If the request is not achievable, communicate limitations and do not declare completion. Require objective metrics and A/B listening before declaring completion. Do not rely solely on objective metrics; corroborate with critical listening. Take listening breaks to avoid ear fatigue during extended reviews. Quick Enhancement Pipeline

1. Analyze original (run preflight and capture baseline metrics)

python3 skills/.experimental/audio-voice-recovery/scripts/preflight_audio.py evidence.wav --out preflight.json

2. Create working copy with checksum

cp evidence.wav working.wav sha256sum evidence.wav

evidence.sha256

3. Apply enhancement

ffmpeg -i working.wav -af "\ highpass=f=80,\ adeclick=w=55:o=75,\ afftdn=nr=12:nf=-30:nt=w,\ equalizer=f=2500:t=q:w=1:g=3,\ loudnorm=I=-16:TP=-1.5:LRA=11\ " enhanced.wav

4. Transcribe

whisper enhanced.wav --model large-v3 --language en

5. Verify original unchanged

sha256sum -c evidence.sha256

6. Verify improvement (objective comparison + A/B listening)

python3 skills/.experimental/audio-voice-recovery/scripts/compare_audio.py \ --before evidence.wav \ --after enhanced.wav \ --format md \ --out comparison.md How to Use Read individual reference files for detailed explanations and code examples: Section definitions - Category structure and impact levels Rule template - Template for adding new rules Reference Files File Description AGENTS.md Complete compiled guide with all rules references/_sections.md Category definitions and ordering assets/templates/_template.md Template for new rules metadata.json Version and reference information