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Given the phase coherence over non-blanked intervals, a simple and effective approach would be to additionally window the non-blank intervals with a matching window corresponding to the length of the time samples in between blanked intervals. Since the window only tapers the amplitude and does not modify the phase, there will be no issue with different ...

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The auditory system encodes sound in frequency domain, i.e. the activation levels of auditory nerve fibers represent the amplitude or energy in a frequency band assigned to that particular fiber. The ear itself does the transformation from time to frequency domain. If you somehow modified the ear itself to be sensitive to higher frequencies, the output axons ...

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Since your input is discrete the spectrum is periodic with the DFT length. $X_0$ is your DC term, but so is $X_{512}$, $X_{1024}$, $X_{-512}$, etc. It really doesn't matter which exact period you pick: [0 511], [-255 256], [-256 255], etc. Any choice will work and you always get the same amount of data. Note that $X_{-256} = X_{256}$.

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Does the Nyquist frequency of the Cochlear nerve impose the fundamental limit on human hearing? No. A quick run-through the human auditory system: The outer ear (pinnae, ear canal), spatially "encodes" the sound direction of incidence and funnel the sound pressure towards the ear drum, which converts sound into physical motions, i.e. mechanical ...

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That link provides a very superficial Q & A on signals and systems. It's not clear which kind of a sampler or quantizer they are referring to. However, if sampling and quantization are defined as ideal mathematical operators then, you should get exactly the same result by altering their orders. Hence the sample values should be same (meaning that the ...

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Approaching The Sampling Theorem as Inner Product Space Preface There are many ways to derive the Nyquist Shannon Sampling Theorem with the constraint on the sampling frequency being 2 times the Nyquist Frequency. The classic derivation uses the summation of sampled series with Poisson Summation Formula. Let's introduce different approach which is more ...

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I wouldn't really call it "bandwidth expansion". If you do 2 samples per symbol, from a purely theoretical point of view (i.e. using perfect infinitely long sinc pulses), you could do with exactly half of the Nyquist bandwidth of the output signal. However, RRC is not infinitely long sincs, so you get excess bandwidth.

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The wavetable is ignorant or agnostic about the playback rate and the sample rate in Hz. What the wavetable knows about is how many points or samples define the waveform, $N$. All it knows is the amplitude and phase of every harmonic (which are two numbers for each harmonic), and with a little bit of nuance regarding the DC component and, if the number of ...

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The fundamental frequency is fixed at $40Hz$, that means you will sample a period of 1/40 second. The Nyquist frequency says that you must sample your signal twice every period for any relevant frequency. If you take $737$ samples, you have 736 periods (the first sample is at time zero). If you take these samples at a constant rate in a period of $1/40$ ...

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