audio enhancement expert
4
总安装量
0
周安装量
#50201
全站排名
安装命令
npx skills add https://github.com/willsigmon/sigstack --skill Audio Enhancement Expert
Skill 文档
Audio Enhancement Expert
Polish podcast and voice audio with AI enhancement tools.
Top Tools
Auphonic (API Available)
- Loudness normalization (LUFS)
- Noise/hum reduction
- Leveling between speakers
- API + Zapier integration
- Pricing: 2 hours free/mo, then pay-per-use
Adobe Speech Enhancer
- Free (1 hour/day limit)
- Web-based at podcast.adobe.com/enhance
- Premium: 4 hours, video support
- Great for quick cleanup
Cleanvoice AI
- Removes filler words (um, uh)
- Background noise removal
- Mouth sounds/clicks removal
- Pricing: Pay-per-minute
Dolby.io
- Enterprise audio APIs
- Real-time enhancement
- SDK integration
- Developer-focused
Auphonic API
Single Production
curl -X POST https://auphonic.com/api/simple/productions.json \
-u "user:pass" \
-F "input_file=@episode.mp3" \
-F "output_basename=episode-enhanced" \
-F "algorithms=loudness,leveler,denoise"
Python SDK
import auphonic
client = auphonic.Client(username="...", password="...")
production = client.create_production(
input_file="episode.mp3",
algorithms=["loudness", "leveler", "denoise", "filtering"],
output_files=[{
"format": "mp3",
"bitrate": 128
}]
)
production.start()
Enhancement Pipeline
Recommended Order
1. Noise reduction (remove constant noise)
2. De-reverb (reduce room echo)
3. Leveling (balance speakers)
4. Compression (even out volume)
5. Loudness normalization (hit target LUFS)
Target Levels
- Podcasts: -16 LUFS (Spotify), -14 LUFS (Apple)
- Audiobooks: -18 to -20 LUFS
- Video: -24 LUFS (broadcast)
Local Processing
FFmpeg Noise Reduction
# Get noise profile
ffmpeg -i input.mp3 -af "silencedetect=n=-30dB:d=0.5" -f null -
# Apply noise reduction
ffmpeg -i input.mp3 \
-af "highpass=f=80,lowpass=f=12000,afftdn=nf=-20" \
output.mp3
SoX Enhancement
# Normalize + noise reduce
sox input.mp3 output.mp3 \
norm -1 \
noisered noise.prof 0.21
Batch Processing
# Auphonic batch
for file in audio_files:
production = client.create_production(
input_file=file,
algorithms=["loudness", "leveler"]
)
production.start()
Use when: Podcast polish, voice clarity, noise removal, loudness normalization