Audio Spectrum Visualizer
Turn any audio file into a spectrogram and frequency spectrum, with dominant frequency, note and tone balance readouts.
Drag & drop an audio file here, or click to browse
No uploads. Your audio stays on your device.
How it works
Drop an MP3, WAV, M4A, OGG, FLAC or WebM file and the Web Audio API decodes it inside this page — nothing is uploaded, so demos, voice memos and unreleased mixes never leave your device. The samples are mixed down to mono, sliced into overlapping windows, and each window is run through a Hann-windowed Fast Fourier Transform written in plain JavaScript. The spectrogram draws time from left to right and frequency from bottom to top, with brightness showing how much energy sits at each frequency (a 90 dB range down from the loudest point).
Switch to Average spectrum for the frequency balance of the whole file at a glance. A bigger FFT size resolves closer frequencies but blurs fast changes; a smaller one does the opposite. The logarithmic scale spreads the low end out the way your ears hear it, which makes bass notes and harmonic stacks easy to read. Long files are sampled evenly across their length so the analysis stays fast, and the readouts — dominant frequency, nearest musical note, spectral centroid (a measure of brightness) and the bass/mid/treble split — come from that same analysis. The 99% rolloff figure is handy for spotting the low-pass shelf a lossy encoder leaves behind, usually around 15–16 kHz for MP3. Save the plot as a PNG or copy the numbers as text.
Frequently asked questions
How do I see the frequency spectrum of an audio file?
Drop an MP3, WAV, M4A, OGG, FLAC or WebM file onto this page. It is decoded with the Web Audio API and run through a Hann-windowed FFT right in your browser, then drawn as a spectrogram (time across, frequency up, brightness for energy) or as an average spectrum. Switch the FFT size for more frequency detail or more time detail, and save the plot as a PNG.
What frequency and musical note is my sound?
The Dominant frequency readout is the strongest frequency across the whole file, refined by parabolic interpolation so it is accurate to roughly a hertz on steady tones, and the Nearest musical note readout converts it to equal-tempered pitch — a 440 Hz tone reads as A4. The spectral centroid next to it is a brightness measure: the energy-weighted average frequency.
Is my audio uploaded anywhere?
No. Decoding, the FFT, the drawing and every readout run entirely in your browser using the Web Audio API and Canvas — there is no server, no account and no ads, so unreleased mixes, interviews and voice memos never leave your device. The tool also works offline once the page has loaded.