How to Detect Fake Lossless Audio Files on Mac

Quick Answer: Open the file in a spectrogram viewer and look at the frequency content above 15 kHz. Genuine lossless files have energy that gradually rolls off toward 22 kHz. Fake lossless files show a hard frequency wall — a flat ceiling where the original lossy encoder cut all data above it. Common cutoffs: 16 kHz (128 kbps MP3), 19 kHz (192 kbps), 20 kHz (320 kbps).

A fake lossless file is exactly what it sounds like: an audio file with a lossless container (WAV, AIFF, FLAC) that actually contains audio compressed from a lossy source (MP3, AAC). The file size looks right. The bit depth looks right. But the audio has already been degraded, and no amount of re-encoding can recover what was lost.

For DJs, this is a real problem. If you're not sure what fake lossless means or how it ends up in DJ libraries, see What Is Fake Lossless Audio? first. Stores occasionally mislabel files. Automated conversion tools silently transcode. Files get passed through workflows that strip or change formats. The result ends up in your library looking like a 24-bit WAV and playing through a club system at 44.1kHz - but it was MP3 the whole time.

Here's how to detect them.

How does fake lossless detection work?

Every lossy compression format (MP3, AAC, OGG) removes high-frequency content to reduce file size. A 128kbps MP3 typically cuts off around 16 kHz. A 320kbps MP3 cuts off around 19-20 kHz. Genuine lossless audio from a CD source extends to 22 kHz (the Nyquist limit for 44.1kHz sample rate).

When someone converts a 128kbps MP3 to WAV or FLAC, the container changes but the audio doesn't. The high frequencies that were removed during MP3 encoding are gone permanently. The resulting file will show a hard cutoff in its spectrogram - a flat, dark region above the cutoff frequency - instead of the gradual rolloff that characterizes real lossless audio.

That cutoff is the fingerprint of a fake.

How do you read a spectrogram?

A spectrogram displays frequency (vertical axis) over time (horizontal axis). Energy is shown as color intensity - bright orange/yellow means strong signal, dark purple/black means silence.

This is what a fake lossless file looks like:

Notice the hard horizontal line at around 16 kHz. Everything above it is dark - no frequency content at all. This file was compressed to MP3 at approximately 128kbps and then converted to a lossless container. The WAV label is technically accurate, but the audio is lossy.

Here's another example - a fake with a cutoff at 16.1 kHz:

This is what a real lossless file looks like:

The frequency content extends toward 20 kHz with a natural, gradual rolloff - not a hard cutoff. Some energy variation above 15 kHz is normal. What you're looking for is the absence of a hard horizontal line.

How do you check files manually for fake lossless?

Using Sonic Visualiser (free)

1. Download and open Sonic Visualiser
2. Open your audio file (File -> Open)
3. Add a spectrogram layer: Layer -> Add Spectrogram
4. Set the frequency scale to linear (right-click the Y axis -> Linear Scale)
5. Zoom in vertically to focus on the 15-22 kHz range
6. Look for a hard horizontal cutoff

This works for individual files. For a library of hundreds of tracks, it's not practical - each file takes 1-2 minutes of manual inspection.

What should you look for in a spectrogram?

Cutoff frequency	Likely source
~16 kHz	MP3 128kbps
~18-19 kHz	MP3 192-256kbps
~19-20 kHz	MP3 320kbps
Natural rolloff at 20-22 kHz	Genuine lossless
Full content to Nyquist (22 kHz)	Genuine lossless

Note: Some recordings have natural high-frequency rolloff due to mastering or the original recording equipment. A cutoff doesn't always mean fake - it requires judgment. Files from vinyl or older recording equipment may have reduced high-frequency content even in genuine lossless form.

How does Spectro detect fake lossless automatically?

For batch processing, Spectro automates the entire workflow. Drop a folder of files, and Spectro runs FFT analysis on each one and returns a verdict:

• LOSSLESS - frequency content extends naturally to the Nyquist limit
• FAKE - hard cutoff detected, consistent with lossy encoding
• MEDIUM - high-bitrate lossy or ambiguous cases (e.g., 320kbps MP3, some masterings)

You can filter the results by verdict, so auditing a library of 200 tracks takes minutes instead of hours.

If you already have years of crates and promos piled up — not just today's downloads — walk through how to systematically audit your existing collection without letting the flagged list overwhelm you.

Spectro is $39 one-time and includes a free trial for the first 100 files.

If you're deciding between Spectro and the free browser-based alternative Fabl, see the full comparison here.

If you want the peer-reviewed breakdown of why this works, see The Science Behind Fake Lossless Audio Detection.

If you used AudioChecker (audiochecker.net) in the past and need a working tool on modern Macs, Spectro is built for the same job — see AudioChecker alternative for Mac.

What are the most common false positives?

Not every frequency cutoff means the file is fake. A few legitimate cases where you might see a cutoff:

Vinyl rips: Records naturally roll off high frequencies due to the RIAA curve and the limitations of the medium. A vinyl rip might show reduced content above 18-19 kHz even when the WAV is a genuine lossless capture.

Heavily mastered electronic music: Some mastering engineers apply brick-wall EQ high-cut filters intentionally. A hard cutoff at 20 kHz on a well-mastered techno track could be intentional, not an artifact of lossy encoding.

Old recordings: Pre-digital recordings from the 1960s-70s may have bandwidth-limited content even in high-quality transfers.

In practice, when you see a cutoff at 16 kHz or lower, it's almost always a fake. Ambiguity increases as you approach 19-20 kHz.

If you want to know whether the files you've bought from stores like Beatport are genuinely lossless, see Is Your Beatport Download Actually Lossless?.

Detecting fake lossless files comes down to one thing: finding the frequency cutoff in the spectrogram. Manual tools like Sonic Visualiser work for individual files. For library-scale auditing, Spectro automates the detection and returns clear verdicts across hundreds of files at once.

Try Spectro free - first 100 files, no account needed ->