audio fingerprint expert

Audio Fingerprint Expert

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

Audio Fingerprint Expert

Identify and match audio content using fingerprinting.

Use Cases for Modcaster

  • Skip intros/outros automatically

  • Detect and skip ads

  • Identify music in podcasts

  • Match duplicate content

Top Services

Commercial APIs

AudD ($2-5/1000 requests)

  • Neural network based

  • Music recognition

  • Real-time and batch

ACRCloud

  • Industry leader

  • Cross-platform SDKs

  • Custom fingerprint databases

ShazamAPI (via RapidAPI)

  • The classic

  • Huge music database

  • Enterprise options

Open Source

AcoustID (Free)

  • Links to MusicBrainz

  • Community-powered

  • Chromaprint fingerprinting

Dejavu

  • Python implementation

  • Self-hosted

  • Custom audio matching

AudD API

Recognize Music

curl -X POST "https://api.audd.io/"
-F "api_token=YOUR_TOKEN"
-F "file=@audio.mp3"
-F "return=spotify,apple_music"

Python

import requests

response = requests.post('https://api.audd.io/', data={ 'api_token': 'YOUR_TOKEN', 'return': 'spotify,apple_music', }, files={ 'file': open('audio.mp3', 'rb'), })

result = response.json() if result['result']: print(f"Found: {result['result']['title']} by {result['result']['artist']}")

AcoustID (Free)

Generate Fingerprint

Install chromaprint

brew install chromaprint

Generate fingerprint

fpcalc -json audio.mp3

Lookup

import acoustid

for score, recording_id, title, artist in acoustid.match(API_KEY, 'audio.mp3'): print(f"Match ({score:.2f}): {title} by {artist}")

Dejavu (Self-Hosted)

Setup

from dejavu import Dejavu

djv = Dejavu(config={ "database_type": "sqlite", "database": "fingerprints.db" })

Fingerprint known audio

djv.fingerprint_directory("known_intros/", [".mp3", ".wav"])

Match unknown audio

songs = djv.recognize(FileRecognizer, "podcast_episode.mp3") print(songs) # Returns matches with timestamps

Podcast Ad Detection Pattern

1. Fingerprint known ads

for ad_file in known_ads: dejavu.fingerprint_file(ad_file)

2. When processing episode

matches = dejavu.recognize(episode_file)

3. Get timestamps of ads

ad_segments = [(m['offset'], m['offset'] + m['duration']) for m in matches]

4. Skip those segments in player

Accuracy Tips

  • Use 10-30 second samples

  • Higher sample rate = better accuracy

  • Noise affects matching

  • Store fingerprints, not audio

Use when: Music recognition, ad skipping, duplicate detection, audio matching

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