---
name: audio-quality-check
description: Analyzes audio recording quality - echo detection, loudness, speech intelligibility, SNR, and spectral analysis. Use when the user wants to check a recording's quality, detect echo or duplication, measure speech clarity, compare original vs processed audio, or diagnose why a recording sounds bad, including tracks from Blackbox or any call recording app.
metadata:
  internal: true
  version: "0.1.4"
  categories: "creative"
  topics: "audio, loudness, snr, speech-intelligibility, spectral-analysis"
  openclaw:
    homepage: https://github.com/tenequm/skills/tree/main/skills/audio-quality-check
    emoji: "🎧"
    requires:
      bins:
        - ffmpeg
    install:
      - kind: brew
        formula: ffmpeg
        bins:
          - ffmpeg
          - ffprobe
---

# Audio Recording Quality Analyzer

Comprehensive audio quality analysis for call recordings. Handles dual-track M4A files (system audio + mic), single-track recordings, and AEC-processed files.

## Quick Start

Run the bundled analysis script on a recording directory:

```bash
python ${CLAUDE_SKILL_DIR}/scripts/analyze_recording.py "/path/to/recording/directory"
```

Modes for focused analysis:
```bash
python ${CLAUDE_SKILL_DIR}/scripts/analyze_recording.py /path --tracks   # track info only
python ${CLAUDE_SKILL_DIR}/scripts/analyze_recording.py /path --echo     # echo detection only
python ${CLAUDE_SKILL_DIR}/scripts/analyze_recording.py /path --quality  # quality metrics (skip echo)
```

For Blackbox recordings, the directory is typically:
`~/Library/Application Support/Blackbox/Recordings/<timestamp-id>/`

## Dependencies

System: `ffmpeg`, `ffprobe` (brew install ffmpeg)
Python: `numpy`, `soundfile`, `scipy`, `pyloudnorm`, `pesq`, `pystoi`, `librosa`

Install all Python deps: `pip3 install numpy soundfile scipy pyloudnorm pesq pystoi librosa`

## What Each Metric Tells You

### EBU R128 Loudness (pyloudnorm)
- **What**: Perceptual loudness in LUFS (Loudness Units Full Scale)
- **Target**: -16 to -24 LUFS for speech
- **Watch for**: AEC/post-processed tracks being significantly louder than originals (indicates the processing is amplifying without normalizing)

### Echo Detection - Autocorrelation
- **What**: Detects delayed copies of the signal within a single track by correlating the signal with itself at various time offsets
- **How to read**: Peaks in the 20-100ms range with correlation > 0.3 indicate signal duplication. The lag tells you the delay of the duplicate copy
- **Key insight**: If you see a consistent peak at the same lag across multiple time segments, that's a systematic duplication (e.g., a virtual audio processor like Krisp introducing a delayed copy at ~53ms)
- **Normal values**: Peaks below 0.15 are typically speech pitch harmonics (harmless). Peaks above 0.3 at consistent lags are echo

### Cross-Track Correlation
- **What**: Measures how much one track's content appears in another (e.g., system audio bleeding into the mic track)
- **How to read**: Values near 0 mean no bleed. Values above 0.1 indicate the mic is picking up system audio
- **Coherence**: Frequency-domain version of the same test. Voice-band coherence (300-3400Hz) is most relevant for speech echo

### PESQ - Speech Quality (requires reference + degraded)
- **What**: ITU-T P.862 standard. Gives a MOS (Mean Opinion Score) comparing a degraded signal against a reference
- **Scale**: 1.0 (bad) to 4.5 (excellent). NB = narrowband (phone quality), WB = wideband
- **Use for**: Comparing AEC-processed mic vs original mic to see if processing helps or hurts
- **Thresholds**: 4.0+ excellent, 3.0+ good, 2.5-3.0 fair, <2.5 poor

### STOI - Speech Intelligibility (requires reference + degraded)
- **What**: Short-Time Objective Intelligibility. Measures how understandable speech remains after processing
- **Scale**: 0.0 to 1.0
- **Thresholds**: >0.8 good, >0.6 fair, <0.6 poor
- **Key insight**: If STOI drops significantly between original and processed, the processing is degrading intelligibility

### Spectral Analysis (librosa)
- **Centroid**: Average frequency weighted by amplitude. Higher = brighter/harsher audio
- **Rolloff (85%)**: Frequency below which 85% of spectral energy sits. Lower = more bass-heavy
- **Zero-crossing rate**: How often the signal crosses zero. Higher = noisier signal. Speech is typically 0.05-0.20; values above 0.30 suggest significant noise

### SNR - Signal-to-Noise Ratio
- **What**: Ratio of speech energy to background noise energy (estimated via energy-based VAD)
- **Thresholds**: >20dB excellent, >15dB good, >10dB fair, <10dB poor
- **Note**: This measures background noise, not echo. A recording can have excellent SNR but still have echo problems

### Per-Minute Energy
- **What**: RMS energy and voice-band energy per minute of recording
- **Use for**: Spotting segments that went silent (mic cut out), got unexpectedly loud (clipping risk), or had activity patterns that help identify when speakers were active

## Manual Analysis Recipes

When you need analysis beyond what the script provides, these patterns are useful.

### Extract individual tracks from dual-track M4A
```bash
ffmpeg -y -i audio.m4a -map 0:0 -ac 1 -ar 16000 /tmp/system.wav
ffmpeg -y -i audio.m4a -map 0:1 -ac 1 -ar 16000 /tmp/mic.wav
```

### Quick loudness check with sox
```bash
sox audio.wav -n stat 2>&1
```

### Check specific time range for echo (Python)
```python
import numpy as np
import soundfile as sf
from scipy import signal

data, sr = sf.read('/tmp/system.wav')
# Analyze 5 seconds starting at 2 minutes
start = 120 * sr
seg = data[start:start + 5*sr]
seg_norm = seg / (np.max(np.abs(seg)) + 1e-10)
autocorr = np.correlate(seg_norm, seg_norm, mode='full')
mid = len(seg_norm) - 1
autocorr = autocorr / autocorr[mid]
# Check 20-100ms range for echo peaks
min_lag = int(0.020 * sr)
max_lag = int(0.100 * sr)
region = autocorr[mid + min_lag:mid + max_lag]
peaks, props = signal.find_peaks(region, height=0.1)
for i, p in enumerate(peaks[:5]):
    lag_ms = (p + min_lag) / sr * 1000
    print(f"  Peak at {lag_ms:.1f}ms, r={props['peak_heights'][i]:.3f}")
```

## Common Issues and What Causes Them

| Symptom | Likely cause | What to check |
|---------|-------------|---------------|
| Speakers sound slightly doubled/echoed | Virtual audio processor (Krisp) creating delayed copy in system audio | Autocorrelation: consistent peak at 40-60ms |
| Mic track has remote speakers' voices | Acoustic echo (speakers to mic) | Cross-track correlation > 0.1 |
| AEC-processed file sounds worse | DTLN-aec degrading signal quality | PESQ/STOI comparing original vs processed |
| AEC-processed file is too loud | Missing loudness normalization after processing | Loudness: processed > -10 LUFS |
| Recording has hiss/noise | Low SNR, noisy mic, or AGC artifacts | SNR < 15dB, high zero-crossing rate |
| Quiet segments mid-recording | Mic cut out or device changed | Per-minute energy: sudden RMS drop |