# Silence Detector

> Detect initial silence segments in audio/video using energy-based analysis. Use when you need to find low-energy periods at the start of recordings (title slides, setup time, pre-roll silence). Use when this capability is needed.

- Skill: `tomevault-io/silence-detector` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/silence-detector`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/silence-detector/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/silence-detector

---


# Silence Detector

Detects initial silence segments by analyzing pre-computed energy data. Finds the transition point from low-energy silence to higher-energy content.

## Use Cases

- Finding initial silence in recordings
- Detecting pre-roll silence before content
- Identifying setup/title card periods

## Usage

```bash
python3 /root/.claude/skills/silence-detector/scripts/detect_silence.py \
    --energies /path/to/energies.json \
    --output /path/to/silence.json
```

### Parameters

- `--energies`: Path to energy JSON file (from energy-calculator)
- `--output`: Path to output JSON file
- `--threshold-multiplier`: Energy threshold multiplier (default: 1.5)
- `--initial-window`: Seconds for baseline calculation (default: 60)
- `--smoothing-window`: Moving average window (default: 30)

### Output Format

```json
{
  "method": "energy_threshold",
  "segments": [
    {"start": 0, "end": 120, "duration": 120}
  ],
  "total_segments": 1,
  "total_duration_seconds": 120,
  "parameters": {
    "threshold_multiplier": 1.5,
    "initial_window": 60,
    "smoothing_window": 30
  }
}
```

## How It Works

1. Load pre-computed energy data
2. Calculate baseline from first N seconds (likely silent)
3. Apply smoothing with moving average
4. Find where smoothed energy exceeds baseline x multiplier

## Dependencies

- Python 3.11+
- numpy

## Example

```bash
# Detect initial silence from energy data
python3 /root/.claude/skills/silence-detector/scripts/detect_silence.py \
    --energies energies.json \
    --threshold-multiplier 1.5 \
    --output silence.json

# Result: Initial silence detected and saved to silence.json
```

## Parameters Tuning

| Parameter | Lower Value | Higher Value |
|-----------|-------------|--------------|
| threshold_multiplier | More sensitive | More conservative |
| initial_window | Shorter baseline | Longer baseline |
| smoothing_window | Less smoothing | More smoothing |

## Notes

- Requires energy data from energy-calculator skill
- Works best when initial period has distinctly lower energy
- Returns empty segments list if no clear transition found
- Output format compatible with segment-combiner

---
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<!-- tomevault:4.0:skill_md:2026-04-11 -->