Audio File Inspector Online Free — Metadata & Spectrogram
A free audio authenticity checker that detects fake lossless and transcoded files. Verifies true encoding quality by analyzing the frequency spectrum — works entirely in your browser.
Inspect any audio file at forensic level — detect fake lossless, check bitrate and quality
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Features
How to Inspect an Audio File
- Upload or drag-and-drop any audio file (MP3, WAV, FLAC, OGG, AAC, M4A, AIFF, WebM, Opus)
- Basic info, metadata, and tags appear instantly
- Expand sections for deep analysis — technical metrics, spectrogram, stereo field, forensics
- Export the full report as JSON for documentation or further analysis
Why Use Our Audio Inspector
Whether you're a music producer checking masters, an audio forensics analyst examining evidence, a podcaster optimizing recordings, or just curious about what's inside your audio files — this inspector gives you the deepest possible look. No registration, no upload limits, no server processing, completely free.
Frequently Asked Questions
Is my audio file uploaded to a server?
No. All analysis happens entirely in your browser using the Web Audio API and raw binary parsing. Your files never leave your device.
What audio formats are supported?
MP3, WAV, FLAC, OGG, AAC, M4A, AIFF, WebM, Opus, and WMA. The inspector reads raw binary data, so even partially corrupted files may yield useful information.
Can it detect if an audio file was re-encoded?
Yes. The inspector analyzes the frequency spectrum for sharp cutoffs typical of lossy encoding. If a WAV or FLAC file shows a cutoff around 16-20 kHz, it was likely converted from a lossy source like MP3. Use our audio converter to re-encode in the correct format.
What is the audio fingerprint?
The inspector generates two SHA-256 hashes: one for the raw file bytes (file identity) and one for the decoded PCM audio data (audio fingerprint). The audio fingerprint stays the same even if metadata changes.
What does the spectrogram show?
The spectrogram is a time-frequency visualization showing how audio energy is distributed across frequencies over time. Bright areas indicate high energy. It reveals patterns invisible in a simple waveform view.
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