pcm-audio

Detect and quantify clipping, silence insertion, pops, and noise floor in PCM audio.

5|5|Updated Apr 11, 2026

Search Tags:#embedded #audio #analyze #clipping #noise #clicks #pcm

Ready to run

npx skills add https://github.com/open-vela/.claude --skill pcm-audio

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pcm-audio
Source: https://github.com/open-vela/.claude/tree/main/skills/pcm-audio
Command: npx skills add https://github.com/open-vela/.claude --skill pcm-audio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

PCM audio quality issues such as clipping, silence insertion, pops, and noise floor in embedded systems, helping you diagnose audio bugs quickly.

Core Features & Use Cases

Clipping detection and reporting with severity indicators
Silence insertion and timing analysis across channels
Pops/clicks detection with time positions
Noise floor estimation and SNR context
Periodic distortion detection via autocorrelation
Optional visualization and programmatic API usage for automation

Quick Start

Run the PCM audio quality analysis tool on a PCM file to get a comprehensive report and optional charts.

Frequently Asked Questions about pcm-audio

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect clipping and pops in PCM audio data?▼

Analyze PCM audio quality to identify clipping with severity indicators and pinpoint exact time positions of clicks. It processes single or multi-channel data to report pops and clipping across embedded audio pipelines.

What causes silence insertion in embedded audio pipelines and how do I find it?▼

Silence insertion in embedded audio pipelines occurs during capture or playback glitches. Run PCM audio analysis to track and report exact silence timing across individual channels.

Can I use this PCM audio analysis tool for multi-channel embedded systems?▼

Yes, you can analyze multi-channel PCM audio in embedded systems. Provide standard parameters like sample rate and channel count to receive structured quality reports for capture and playback debugging.

Does the audio quality analyzer estimate noise floor and SNR context?▼

Yes, the audio quality analyzer estimates noise floor and provides SNR context. It also detects periodic distortion via autocorrelation to help diagnose embedded audio bugs comprehensively.

Do I need numpy and scipy to run the PCM audio quality analyzer?▼

Yes, numpy and scipy are required dependencies to run the PCM audio quality analyzer. These libraries support the signal processing and autocorrelation calculations needed for distortion detection.

What is the best way to automate PCM audio quality reports for debugging?▼

The best way to automate PCM audio quality reports is via the programmatic API. It returns structured results with optional visualizations, enabling automated detection of clipping, noise, and silence issues.