Track Analyzer icon
Track Analyzer icon

Track Analyzer

Analyze BPM, key & danceability in 1s and auto-tag audio files on Mac. Offline Mixed In Key & beaTunes alternative with auto-rename.

Track Analyzer screenshot 1

Cost / License

  • Pay once
  • Proprietary

Platforms

  • Mac  Requires macOS 14.0 or later
0likes
0comments
0articles

Features

Properties

  1.  Lightweight

Features

  1.  Batch Rename Files
  2.  Works Offline
  3.  Dark Mode
  4.  MP3 / ID3 Renaming
  5.  No registration required
  6.  No Tracking
  7.  Batch Editing
  8.  Ad-free
  9.  Built-in viewer
  10.  Audio Analysis
  11.  Music Library
  12.  Automatic Tagging
  13.  Key Detection
  14.  Offline
  15.  File Renaming
  16.  BPM Detection

Track Analyzer News & Activities

Highlights All activities

Recent activities

Track Analyzer information

  • Developed by

    FR flagRebels
  • Licensing

    Proprietary and Commercial product.
  • Pricing

    One time purchase (perpetual license) that costs $5.
  • Alternatives

    11 alternatives listed
  • Supported Languages

    • English

AlternativeTo Categories

Audio & MusicFile Management
Track Analyzer was added to AlternativeTo by Rebels on and this page was last updated .
No comments or reviews, maybe you want to be first?

What is Track Analyzer?

Track Analyzer is a Mac-native audio analysis tool that detects BPM, musical key, and danceability in under 1 second per track. It also writes the results directly into the file's metadata and renames audio files using your custom format, all offline.

The only Mac app that combines fast analysis, automatic tagging, and file renaming in a single offline workflow. Essential for DJs, producers, and music library managers who need reliable data without an internet connection.

Key Features

• Instant analysis: BPM, musical key, and danceability detected in under 1 second • Auto-tagging: writes BPM, key, and danceability directly into audio file metadata • Auto-rename: renames files using fully customizable naming formats • Professional algorithms: industry-standard detection, not consumer-grade approximations • Fully offline: local processing, no cloud upload, no AI learning on your files • Mac-native: built specifically for macOS with minimal resource usage