🎛️ Noise Colors Explained

About this simulation

"Noise" in signal processing is a random signal, and its color describes how its power is distributed across frequency — by analogy with visible light, where blue light carries more energy per photon than red. Each bar in this 3D chart is a frequency band; its height is that band's average power. Switching noise colors changes the shape of the spectrum, not just its loudness.

How it works

• White noise — equal power at every frequency (flat spectrum, exponent 0). Sounds like a hiss or TV static.
• Pink noise — power falls off as 1/f (exponent −1), so each octave carries equal energy. Sounds duller and more balanced, like steady rainfall.
• Brown (red) noise — power falls off as 1/f² (exponent −2), even more bass-heavy. Produced by integrating white noise over time — like a random walk. Sounds like a deep rumble or waterfall.
• Blue noise — power rises with frequency, +3dB per octave (exponent +1). Sounds thin and hissy, emphasizing highs.

Controls

• Noise color — choose which power-law spectrum to display and hear.
• Spectrum bars — number of frequency bins shown across the chart.
• Jitter speed — how fast each bar's instantaneous power flickers around its target curve, mimicking a live analyzer.
• Volume — loudness of the synthesized noise audio when sound is playing.
• Log frequency scale — toggle between a logarithmic (like a real spectrum analyzer) and linear frequency axis.
• 🔊 Play sound — synthesizes and loops the selected noise color through the Web Audio API so you can hear the difference, not just see it.

Did you know?

Pink noise's equal-energy-per-octave property matches how human hearing perceives loudness, which is why it's favored for audio equipment testing and sleep aids — while brown noise's deep rumble is a popular focus and relaxation sound.