Search Results for "audio fingerprinting"

ThumbmarkJS is an MIT-licensed browser fingerprinting library that produces stable fingerprints with 90% uniqueness. It works with normal and private browsing. ThumbmarkJS is a free, open‑source browser fingerprinting JavaScript library, designed as an alternative to FingerprintJS. It generates distinct, persistent device fingerprints using web APIs like canvas, audio, fonts, WebGL, and more, enabling identification of browsers across sessions, even in incognito or cleared-cache scenarios. ...

...The system continuously monitors selected artists and detects new releases, then generates personalized playlists such as Release Radar and Discovery Weekly using its built-in recommendation logic. It can automatically download missing tracks from multiple sources including Soulseek, YouTube, and Beatport, then verify file accuracy through AcoustID fingerprinting to ensure the correct audio was obtained. The platform also enriches tracks with lyrics, album art, and MusicBrainz metadata while organizing files into clean, customizable folder structures. Once processed, the library can be synchronized with media servers like Plex, Jellyfin, or Navidrome so collections stay up to date without manual intervention.

...First scan may take a while depending on your PC and number of audio files, but on subsequent scans only newly added files will be scanned. IL Music Library Deduplicator is portable and doesn't require instalation and also is free. It is written in Python and is using Chromaprint fpcalc for audio fingerprinting. It can find duplicates even if audio files have no metadata or have a different filename.

Console and library-based windows app for tagging of media files. Supports adding full tags, MusicBrainz tagging, and music fingerprinting.

This library is a Java port from the libFooID audio fingerprinting library created by Gian-Carlo Pascutto. The fingerprint captures how the audio is perceived by an audience so it can e.g. be used in a database to retrieve metatags of a track.

Automated Attendance System (AAS) uses 2 modes for authentication - * Voice Identification System (VIS) * Fingerprinting Method The algorithms used for the same has been developed by me. This algo is more efficient and faster.