I had heard that tape is still the best medium for storing large amounts of data. well, "best" is always a reduction to a single set of optimization parameters (e.g. cost per bit, ...

Citing Bellanger's classic Digital Processing of Signals – Theory and Practice, the point is not where your cut-off frequency is, but how much attenuation you need, how much ripple in the signal you ...

Why then we still have $\Delta^2 \over 12$ quantization noise You need to read more closely: this is the quantization noise for continuous-uniformly distributed signal amplitudes and equally spaced ...

Does cubic interpolation (or any other) have any advantages over linear for the specific case of audio? You'd use neither for audio. The reason is simple: The signal models you typically assume for ...

You'll need to understand the sampling theorem. In short, each signal has what we call a spectrum¹, which is the Fourier transform of the signal as it comes in time domain (if it is a time signal), or ...

If you consider poles of an integral transform domain to be important to the solution of differential equations: (as usual,) Euler did it first, 1753. One "importance" of poles is that they're part of ...

From the wikipedia article: a stochastic process is said to be ergodic if its statistical properties can be deduced from a single, sufficiently long, random sample of the process. In other words: ...

So it seems like real-world (discrete) audio signal might have complex values when being represented digitally, No, you misunderstood that. The discrete audio time signal doesn't have non-real ...

All the three devices you mention have mixers, so that's what you wouldn't find in an oscilloscope usually. The amplifiers are optimized for weaker signals, and typically, the quality of ADCs in ...

I'm afraid there won't ever be a rule of thumb! The reasons are manifold, mainly, that both the systems you're considering and the problems you're trying to solve vary across a very large range. ...

"Zero-Mean" is the word that's commonly used to describe signals and signals with a zero average. "This is a zero-mean filter." If you really mean a filter that is specifically ...

To illustrate Justme's answer: Discrete Cosine Transform (DCT) is a lossy The DCT can't be a lossy algorithm, since there's an inverse operation that restores the original input exactly. data ...

Is it possible to reconstruct the original pure signal? No, that is information-theoretical impossible. Also, that signal doesn't exist, probably, to begin with ;) However, you can definitely ...

Assume you have a $N\times M$ sized image. If you know take what is classically used, a square filter kernel, of let's say size $L\times L$, you'd need to convolve that with the picture – which gives ...

I am currently implementing acoustic FSK modulation and demodulation. I am not a signal processing guy… Since you say you have matched filters, and you mention non-coherent detection, I think you're ...

If you're an EE student, you will have encountered the term LTI System (or you certainly will soon enough!): A system that, no matter the absolute time, outputs, given the same input, the same output; ...

Since this is a constant spectrogram, you could just as well have just averaged the |FFT|² and plotted that! (The most colorful way of visualizing things isn't always the optimal one; your signal ...

well, have you ever seen a rotating barber's poll? It looks like the stripes are moving up (optical flow), but of course the motion of the thing is a rotation in the horizontal plane. The rationale ...

The problem that I have is that I always have a big spike (10-15 dB) directly on the center frequency (no matter what frequency I set). I am relatively new to all this so I would appreciate any ...

You're spot on: whereas other frequencies are typically subject to noise that is somewhat benign shaped and result of random processes, DC is usually affected by things like a DC offset. Physically, ...

Short answer: You can't. If an attacker can insert a signal that covers the whole bandwidth (e.g. a white signal, or at least one that has no spectral zeros) into the system (and he can do that over ...

If you look at the formula of a single DFT bin $$X[k] = \sum_{n=0}^{N-1}x[n]e^{-j2\pi k\frac nN}\text,$$ you'll notice that his is essentially a correlation of $x$ with the complex sinusoid \$e^{-j2\...

Your signal recording clearly shows that you have long streaks of 1.0 – that probably means you're clipping. Your signal is thus broken. Make a new recording with less gain.

At long distances, your transmit signal loses a lot of power. As a result of that, the SNR at the receiver is possibly relatively low. A low SNR means that you cannot transport many bits per second ...

More of a terrible visual pun: The FFTiramisu.

There's nothing wrong here - complex sinusoids like your signal really have only one peak in frequency domain! This is the fundamental idea of why we use the Fourier transform for periodic (even ...

Do I have to filter the whole (or at least a huge bit) of the signal every time a few new samples came in or is there a way (like the sliding DFT) where it is possible to efficiently determine the new ...

Simply because the highest compression typically is significantly more CPU-intense (it tries out multiple different approaches to represent successive lines). This really shouldn't make much ...