That's a completely reasonable thing to do, especially because it might allow you to pick up distortion due to interpolation.
However, you should do the same thing to the white noise [1] if you are going to compare them. If we write the white noise's continuous reconstruction [2] as a function of time, w(t), we could stretch out w(t) until it wiggles at about the same rate as the perlin noise, p(t), and then sample them together at a rate several times higher than that at which they wiggle. Both waveforms would then have the same "muffled roar" sound you get in airplanes, buildings, underwater, etc.
Another mundane explanation for the bands is that they might be "JPEG artifacts" for ogg's compression. Amplitude is logarithmic, so they're probably not as important as they look.
[1] To be pedantic we should call it band-limited white noise, because the sampling+reconstruction process limits the bandwidth, and infinite bandwidth white noise can't actually exist, because it has finite energy per bandwidth * infinite bandwidth = infinite energy. This isn't a theoretical problem. Oscilloscopes have fatter "no-signal" traces in proportion to their bandwidth, the resolution bandwidth ("RBW") of spectrum analyzers lifts the noise floor at higher settings, the field of thermodynamics fell apart in the "ultraviolet catastrophe" before we understood how quantum mechanics effectively limits the bandwidth of thermal radiation, etc.
[2] w(float t) rather than w(int n), obtained by interpolation. Sinx/x interpolation is the interpolation that gives 0 distortion and produces no higher spectral content. It's the time domain equivalent of doing a Fourier Transform, scaling the spectrum, and doing an Inverse Fourier Transform. IIRC Perlin noise is a spline, not sinx/x, so I'd expect its interpolation to produce higher harmonics. By applying perlin-like (spline?) interpolation and sinx/x interpolation to the white noise, you could isolate the audio effects due to the randomization vs the interpolation of the perlin noise. If you were so inclined :)
However, you should do the same thing to the white noise [1] if you are going to compare them. If we write the white noise's continuous reconstruction [2] as a function of time, w(t), we could stretch out w(t) until it wiggles at about the same rate as the perlin noise, p(t), and then sample them together at a rate several times higher than that at which they wiggle. Both waveforms would then have the same "muffled roar" sound you get in airplanes, buildings, underwater, etc.
Another mundane explanation for the bands is that they might be "JPEG artifacts" for ogg's compression. Amplitude is logarithmic, so they're probably not as important as they look.
[1] To be pedantic we should call it band-limited white noise, because the sampling+reconstruction process limits the bandwidth, and infinite bandwidth white noise can't actually exist, because it has finite energy per bandwidth * infinite bandwidth = infinite energy. This isn't a theoretical problem. Oscilloscopes have fatter "no-signal" traces in proportion to their bandwidth, the resolution bandwidth ("RBW") of spectrum analyzers lifts the noise floor at higher settings, the field of thermodynamics fell apart in the "ultraviolet catastrophe" before we understood how quantum mechanics effectively limits the bandwidth of thermal radiation, etc.
[2] w(float t) rather than w(int n), obtained by interpolation. Sinx/x interpolation is the interpolation that gives 0 distortion and produces no higher spectral content. It's the time domain equivalent of doing a Fourier Transform, scaling the spectrum, and doing an Inverse Fourier Transform. IIRC Perlin noise is a spline, not sinx/x, so I'd expect its interpolation to produce higher harmonics. By applying perlin-like (spline?) interpolation and sinx/x interpolation to the white noise, you could isolate the audio effects due to the randomization vs the interpolation of the perlin noise. If you were so inclined :)