How to interpret these different Fourier analysis of this audio signal? This is my first dive in DSP. I would like to familiarize myself with frequency analysis. I have two audio tracks which should be digitized at 16bit-44.1kHz and 24bit-192kHz (music, presented as a 24bit-192kHz sample) respectively.

I wanted to identify the effect of the low-pass filter around the Nyquist frequency (22.05kHz and 96kHz respectively).

Edit: I completely reworked the question.



Software used:

I basically estimated the power spectral density using Welch's method 
as implemented by scipy.signal.welch in the Scipy library of the Python programming language.

Basically, I used a script equivalent to:

import numpy as np
from matplotlib import pyplot as plt
from scipy import signal
from waveio import readwav

# Load data from one channel (#0) for each sample file 
wav192 = readwav("24b-192khz.wav")[:,0]
wav44 = readwav("16b-44khz.wav")[:,0]

# DoE: 2 sample size and two windows types
chunks = [256, 4096]
windows = ["hanning", "boxcar"] # boxcar is rectangular

# Prepare a figure
plt.figure()
# Calculate density spectra and plot
for N in chunks:
    for w in windows:
        f, Pxx44 = signal.welch(wav44, fs=44100, window=w, nperseg=N, nfft=2*N, scaling="density")
        plt.semilogy(f, Pxx44)
plt.legend(["chunk=%d; window=%s"%(c, w) for c in chunks for w in windows])
plt.xlabel("Frequency (Hz)")
plt.ylabel("Density (I$^2$/Hz)")




The power spectral density of the 44.1kHz audio sample:



Which is basically just as expected:


The chunk size, i.e. the number of samples per fft-transform segment in the real domain, does not change the density a lot if a Hann window is used.
The chunk size effect is clearly visible with the boxcar (rectangular) window. From what I understand, this is because of spectral leakage which diminishes as the chunk size increases. Is that correct?
The low-pass filter effect at the Nyquist frequency (22.05kHz)  


So far, so good.



The power spectral density of the 192kHz audio sample:



Good point:


Same behaviour in regard to the chunk size and the window. Is spectral leakage really that strong? That's pretty impressive.


Oddities:


What the heck is happening? 
Where is the low-pass filter near the Nyquist frequency?
Why are very-high frequencies even increasing? Could that be related to the choice of the windowing function?


From my interpretation, there is no low-pass filter visible because basically no audio system would go above 192kHz and generally, the software/hardware creator are smart enough to apply a low-pass filter designed with regard to the actual output bandwidth of the audio system.

As for the increasing audio signal above 57kHz, I really can't explain it: the original audio sample is some classical music. I wouldn't expect any instrument to generate louder sounds in that range or frequencies. Any idea? Could this be an example of upsampling?
If you look at the Rectangular window the best its rejection gets is about 40 dB. So that behavior, especially obvious in the bottom plot, for the rectangular window is to be expected.

I don't know for sure if this explains everything but look at the level between your peak signal and the high-frequency components. There is almost 60 dB of rejection there. I've always heard that a good rule of thumb is to get 60 dB of rejection from your filters. I know that's not the full 96 dB offered by 16-bit music, but I bet you'd be hard pressed to actually hear that. Of course, there's the silliness of having music at that sample rate. Humans just can't hear anything above around 20 kHz, give or take. This article gives a good summary of the issues. It also brings up a good point, that that sample rate has the potential to pick up harmonics and other high-frequency distortion caused by equipment and electronics, despite the fact that we can't hear it. Perhaps something like that is going on?