To quote someone else: "White noise is a sound that contains every frequency within the range of human hearing (generally from 20 hertz to 20 kHz) in equal amounts. Most people perceive this sound as having more high-frequency content than low, but this is not the case. This perception occurs because each successive octave has twice as many frequencies as the one preceding it. For example, from 100 Hz to 200 Hz, there are one hundred discrete frequencies. In the next octave (from 200 Hz to 400 Hz), there are two hundred frequencies."
"Pink noise is a variant of white noise. Pink noise is white noise that has been filtered to reduce the volume at each octave. This is done to compensate for the increase in the number of frequencies per octave. Each octave is reduced by 6 decibels, resulting in a noise sound wave that has equal energy at every octave." Therefore my understanding of how you would create pink noise is to 2nd order low pass filter white noise, with the 6dB point (cutoff frequency) at 200Hz. The second order filter will give you the 6dB/ocatave dropoff as frequency increases. If you have LabVIEW 7 you may do this with two express VI's. Here's what I did: - Express VI: Simulate Signal - Signal type: DC - Offset: 0 - Check the option for Add Noise: White Noise / Gaussian White Noise - Set Samples per second (Hz) to: 44100 (This gives us good CD quality white noise :) - Express VI: Filter - Type: Lowpass - Cutoff Frequency (Hz): 200 - IIR/FIR: IIR - Topology: Butterworth - Order: 2 When these are finished, wire the Simulate Signal--DC with Gaussian Noise Output to the Filter--Signal input. The output of the filter will be pink noise. For comparison use two of the Spectral Measurement Express VI, one for the input signals to the filter (white noise) and one for the output signals (pink noise). Configure these to show power spectrum or power spectral density and create graph indicators for their outputs. Map the x-axis of these graphs to logarithmic, auto-scaling off (with the same scale values for both), and you will see that the filter input signal has a fairly constant spectral density, while the output signal has a fairly linear decay as frequency increases.
