Preparing your comprehensive developer toolkit
Practical Guide
Visualize audio frequency spectrum and waveform in real-time with FFT analysis.
Audio spectrum analysis uses FFT to estimate the strength of frequency bands in a playing recording. This tool also shows a waveform; its peak readout is a frequency-bin estimate, not a calibrated pitch or loudness measurement.
Fast Fourier Transform (FFT) converts time-domain audio signals into frequency-domain representation, revealing frequency components. AnalyserNode.getByteFrequencyData() provides frequency bins where each bin represents frequency range. FFT size determines resolution: larger values (2048, 4096) provide finer frequency detail but slower updates; smaller values (256, 512) enable real-time visualization.
The waveform plots current time-domain samples from AnalyserNode. It helps show changes in amplitude and shape, but does not measure calibrated loudness or prove clipping.
Browser Web Audio API enables audio processing: AudioContext creates audio graph, MediaElementSource connects audio elements, AnalyserNode extracts frequency/time data, destination outputs to speakers. The API supports: real-time processing, audio effects (filters, reverb), audio synthesis (oscillators), and recording (MediaRecorder). Use cases: music players with visualizations, DJ applications, audio editing tools, voice analysis, and educational demonstrations of sound properties.
Shows FFT frequency bins while a local audio file plays.
Shows the current time-domain waveform during playback.
Switch views and adjust FFT size or smoothing.
Play common audio formats when supported by your browser.
Compare the visible balance of low, mid, and high frequencies while a local mix plays. Use the result as a visual reference when reviewing EQ choices.
Play recordings one at a time to compare their visible frequency balance and waveform changes. This is not a live room-response or PA calibration tool.
Play a speech recording to inspect its changing spectrum and waveform. Use listening and dedicated meters for intelligibility, noise, or loudness decisions.
Demonstrate FFT, harmonics, envelopes, and filter effects in classrooms or workshops with real-time visuals learners can relate to.
FFT (Fast Fourier Transform) is a mathematical algorithm that converts time-domain audio signals (waveform) into frequency-domain representation (spectrum). It breaks down complex audio into individual frequency components, allowing you to see which frequencies are present and their amplitudes. The FFT size parameter determines the frequency resolution: larger values (2048, 4096) provide finer detail but slower updates, while smaller values (256, 512) enable real-time visualization.
Waveform shows amplitude (loudness) over time on the Y-axis, displaying the shape of the audio signal. Spectrum shows frequency content on the X-axis and amplitude on the Y-axis, revealing which frequencies are present at any moment. Waveform is useful for seeing dynamics and rhythm, while spectrum is essential for analyzing pitch, harmonics, noise, and frequency balance in audio.
MP3, WAV, OGG, and M4A may work if your browser can decode them. There is no fixed limit in this page; very large files may load slowly or fail depending on the browser and device.
Use the spectrum to compare dominant frequency bands and the waveform to inspect changing amplitude while a recording plays. This is a visual aid, not a calibrated loudness, clipping, or acoustic measurement.
Overview of browser audio processing.
Reference for frequency and time-domain analysis.
Practical guide to drawing spectrum and waveform charts.
Technical specification for Web Audio processing.
Open the tool to try these features.