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


signal-
noise-
spectral-
voice-
temporal-
transcribe-
forensic-
tool-

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:

bash

# 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