Free Online Spectrogram

Real-time audio frequency visualizer and spectrum analyzer. Inspect live microphone acoustics, upload local audio files (MP3, WAV, FLAC), and measure harmonic overtones directly in your browser.

Real-Time Spectrogram

Time is plotted on the horizontal axis, frequency on the vertical axis, and sound amplitude is displayed via heatmap colors.

Upload Audio File

Freq: --- HzNote: ---Level: --- dB

Peak:0 Hz

Audio Visualizer Ready

Click microphone, upload an audio file, or play a demo sample to start.

Palette

FFT Window

Scale

Max Frequency: 20 kHz

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How It Works

Three simple steps to start analyzing audio frequencies in your browser.

Step 01

Choose Audio Source

Select "Start Microphone" for live acoustic measurement, "Upload Audio File" for local MP3/WAV tracks, or play a synthetic demo sample.

Step 02

Select Visualizer Mode

Switch between Spectrogram (waterfall frequency over time), Spectrum Analyzer (real-time FFT bars), or Waveform (time-domain oscilloscope).

Step 03

Inspect & Save

Hover over the display to inspect exact frequencies in Hertz and decibels. Click Freeze to pause, or Export PNG to save your chart.

Built for Accurate Frequency Analysis

Everything needed to visualize and analyze audio frequencies without installing desktop software.

Microphone & Audio File Input

Capture live mic input in real time or upload local MP3, WAV, FLAC, and OGG tracks for instant spectral inspection.

Zero-Latency Web Audio FFT

Processes signals directly in the browser via the HTML5 Web Audio API and Fast Fourier Transform algorithms at 60 FPS.

100% Client-Side Privacy

No sound data or voice recordings leave your device. All DSP calculations execute locally in memory on your CPU.

Scientific Heatmap Palettes

Toggle between industry-standard color palettes including Inferno, Viridis, Magma, and High-Contrast monochrome.

Interactive Frequency Inspector

Hover anywhere over the canvas to view exact frequency in Hertz (Hz), pitch note, and decibel amplitude under your cursor.

Image Snapshot Export

Freeze and export clean, high-resolution PNG image snapshots for research, mastering, or acoustic documentation.

What is a Spectrogram?

A spectrogram (also referred to as a sonogram, spectral waterfall display, or voiceprint) is a visual representation of the spectrum of frequencies in an audio signal as they evolve over time. Unlike a standard audio waveform that only displays amplitude (loudness) across time, a spectrogram adds a critical third dimension: frequency distribution.

X-Axis

Time Dimension

Displays elapsed time in seconds as the audio signal streams from left to right.

Y-Axis

Frequency (Hertz)

Plots pitch from deep sub-bass (20 Hz) up to ultrasonic frequencies (20,000 Hz).

Z-Axis

Amplitude (dB)

Heatmap intensity indicates energy. Darker areas represent silence; bright yellow/white indicates loud harmonic peaks.

How Short-Time Fourier Transform (STFT) Generates a Spectrogram

Digital audio streams in as raw pressure samples over time. To convert these samples into visible frequencies, our tool uses the Short-Time Fourier Transform (STFT):

Windowing: The continuous stream is segmented into overlapping mathematical chunks (windows) using a Hann or Blackman window to eliminate boundary artifacts.
Fast Fourier Transform (FFT): The FFT algorithm decomposes each segment into its component sinusoidal frequencies, computing magnitude for each frequency bin.
Color Mapping: The magnitude (in decibels) of each bin is mapped to a color value and plotted as a vertical pixel stripe.
Waterfall Rendering: Frames are drawn continuously at 60 FPS to produce a smooth, scrolling waterfall display.

Common Applications of Spectrogram Analysis

Music Production & Mastering

Audio engineers use spectrograms to locate muddy frequencies (200-400 Hz), harsh sibilance in vocals (5-8 kHz), and stereo phase issues.

Speech Therapy & Linguistics

Phoneticians analyze vowel formants (F1, F2 resonant frequencies in the vocal tract) to evaluate articulation and accents.

Bioacoustics & Wildlife Biology

Ornithologists and marine researchers identify bird calls, bat echolocation, and whale songs by matching their visual spectral fingerprints.

AI & Machine Learning

Speech-to-text models (such as Whisper) and neural music generators convert audio into Mel-spectrograms as primary input feature representations.

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Frequently Asked Questions

What is a spectrogram and how does it work?

A spectrogram is a visual representation of the spectrum of audio frequencies as they vary over time. Time is displayed along the horizontal X-axis, frequency in Hertz along the vertical Y-axis, and color intensity or brightness represents the signal amplitude (volume or power in decibels).

What is the difference between a spectrogram and a spectrum analyzer?

A spectrum analyzer displays frequency distribution at a single instant in time (typically as vertical bars or an FFT line curve). A spectrogram displays how those frequency spectrums evolve continuously over time in a 2D or 3D waterfall display.

Is this online spectrogram analyzer free and private?

Yes, SpectrogramAnalyzer.online is 100% free and processes all audio directly within your web browser using HTML5 Web Audio and WebGL/Canvas. No audio files or microphone streams are ever uploaded to remote servers or stored in any database.

What audio formats can I upload for spectrogram analysis?

You can upload common digital audio formats including MP3, WAV, FLAC, OGG, M4A, AAC, and WebM. All formats supported natively by your browser's audio decoder will render immediately.

What does FFT window size mean in audio spectrum analysis?

Fast Fourier Transform (FFT) size determines the trade-off between frequency resolution and time resolution. A higher FFT size (such as 2048 or 4096) provides very precise frequency bins to separate closely-spaced musical notes, while a lower FFT size (such as 512) responds faster to rapid transient drum hits.

Can I use this spectrogram to find hidden images in audio?

Yes! Audio steganography artists (such as Aphex Twin and Venetian Snares) embed visual pictures into songs by converting grayscale pixels into audio frequencies. Uploading such tracks to this spectrogram in linear frequency mode will reveal the hidden visual artwork.