audio-quality-check

Solid

Analyze audio recording quality - echo detection, loudness, speech intelligibility, SNR, spectral analysis. Use when the user wants to check a recording's quality, detect echo or duplication in audio files, measure speech clarity, compare original vs processed audio, diagnose why a recording sounds bad, or analyze audio tracks from Blackbox or any call recording app. Triggers on audio quality, recording analysis, echo detection, check recording, sound quality, analyze audio, speech quality, PESQ, STOI, loudness, SNR, audio diagnostics, recording sounds bad, echo in recording, audio duplication.

AI & Automation 31 stars 2 forks Updated 3 days ago MIT

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Quality Score: 85/100

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Frontmatter 20%
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Documentation 15%
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Issue Health 10%
80
License 10%
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Description 5%
100

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 <skill-path>/scripts/analyze_recording.py "/path/to/recording/directory" ``` Modes for focused analysis: ```bash python <skill-path>/scripts/analyze_recording.py /path --tracks # track info only python <skill-path>/scripts/analyze_recording.py /path --echo # echo detection only python <skill-path>/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 i...

Details

Author
tenequm
Repository
tenequm/skills
Created
8 months ago
Last Updated
3 days ago
Language
Python
License
MIT

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