Spectrogram Generator
Render an audio file's frequency content over time as a spectrogram image.
What is Spectrogram?
A spectrogram renderer for inspecting audio content: spotting a low-cut rumble, a 50 Hz hum, a clipped recording, or the frequency ceiling of a lossy file.
How it works
A short-time Fourier transform runs on the decoded samples with your chosen window size, hop and frequency cap. The resulting magnitude grid is mapped through a colour ramp — one pixel column per time frame, one row per frequency bin.
- Load your file. Drag a file onto the drop zone or pick one from your device. It is read locally by the browser and never uploaded.
- Set the options. Adjust the handles, format, quality or threshold controls — the preview updates as you change them.
- Process and download. Run the tool, then download the result. Compare the reported before/after size for compressors and converters.
Examples
A 42 MB podcast episode → 9 MB MP3
Trim the intro, normalise to −16 LUFS, and export at 96 kbps mono — a typical interview lands around a fifth of the original size.
A phone video with the wrong orientation
Rotate 90° and the width/height swap in the output, so it plays upright in every player instead of relying on metadata flags.
Common mistakes
Expecting instant encoders
Video encoding runs in single-threaded WebAssembly, so a clip takes roughly its own length (or a little longer) to re-encode. Stream-copy operations are near-instant by comparison.
Re-encoding when a copy would do
Trimming, removing audio and joining same-codec clips can copy streams without re-encoding — pick the copy option when it is offered to avoid any quality loss.
Ignoring the first-load download
The media engine is about 31 MB and is fetched on first use, then cached. The first conversion on a connection is slower than every later one.
Frequently asked questions
What can a spectrogram tell me?
Where the energy sits across the frequency range: room rumble below 80 Hz, harshness around 3–5 kHz, hiss above 10 kHz, and the cut-off frequency that reveals a file's true source quality.
What window size should I use?
2048 samples is a good general default. Smaller windows show sharper timing for transients; larger ones separate closely spaced frequencies for musical analysis.
Why is the top of the image flat and dark?
The recording has no energy up there — typical of MP3s and telephone calls, which roll off the high frequencies.