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🎵 Audio Fingerprint Comparator

Upload audio files to analyze waveforms, spectrograms, RMS/peak levels, entropy, and zero-crossing rate. Compare two audio files to detect if one is a manipulated version of the other — useful for deepfake audio detection and copyright analysis.

🎵 Drop audio A or click

Analyze, Fingerprint, and Compare Audio Files for Authenticity and Manipulation

The Audio Fingerprint Comparator brings professional-grade audio forensics to your browser. Whether you need to verify the authenticity of a recording, detect deepfake audio, match two clips from the same original source, or analyze the acoustic fingerprint of an unknown audio file — this tool handles it all without uploading a single byte to external servers.

The tool generates a frequency spectrogram — a visual map of which frequencies are present at which times — making voice splices, inserted silences, and cloned audio segments immediately visible. The waveform view shows amplitude over time, revealing abrupt level jumps that indicate cuts. Shannon entropy measures the informational complexity of the audio, with natural recordings exhibiting higher entropy than synthesized or artificially generated audio.

In Compare Mode, two audio files are analyzed in parallel. The similarity score is computed from RMS level correlation, zero-crossing rate patterns, and spectral centroid alignment — the same metrics used by professional audio forensics labs.

⚡ How to Use

  1. 1Upload an audio file (MP3, WAV, OGG, FLAC, M4A) using the dropzone.
  2. 2The waveform and spectrogram render automatically after loading.
  3. 3Review the entropy score: values below 0.4 suggest synthetic or heavily processed audio.
  4. 4For deepfake detection, look for unnatural flatness in the 4–8 kHz frequency band.
  5. 5Use Compare Mode to upload a second file and compute the acoustic similarity score.

🎯 Who Uses This

  • ▸Legal teams verifying whether a voice recording has been edited or spliced
  • ▸Journalists authenticating leaked audio before publication
  • ▸Musicians detecting unauthorized sampling of their work
  • ▸Podcast producers identifying audio quality issues in guest recordings
  • ▸Law enforcement examining 911 call recordings for authenticity
  • ▸Researchers studying deepfake audio detection algorithms

Frequently Asked Questions

Q: Can this tool detect AI voice cloning (deepfakes)?
AI voice cloning tools often produce audio with characteristic spectral artifacts — reduced high-frequency content, unnatural pitch stability, and low entropy. The spectrogram and entropy metrics help identify these patterns, though no tool catches 100% of deepfakes as generation technology improves continuously.
Q: What is Shannon entropy in the context of audio?
Shannon entropy measures the informational "randomness" of an audio signal. Natural environmental recordings and authentic speech have high entropy. Synthesized speech, looped sounds, and AI-generated audio tend to exhibit lower entropy due to their mathematical regularity.
Q: Does the audio get sent to any server?
No. The Web Audio API in your browser handles all decoding and analysis locally. Nothing is uploaded, streamed, or stored externally.