audio-quality-check

Solid

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.

AI & Automation 36 stars 1 forks Updated today MIT

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Skill Content

# 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 c...

Details

Author
tenequm
Repository
tenequm/skills
Created
10 months ago
Last Updated
today
Language
Python
License
MIT

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