audio enhancement expert

Audio Enhancement Expert

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Install skill "audio enhancement expert" with this command: npx skills add willsigmon/sigstack/willsigmon-sigstack-audio-enhancement-expert

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

Source Transparency

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