# Tag Info

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My understanding of your question is that you have a small misunderstanding here. In the definition it is not said time and space, a signal can vary in time or space. Some signals vary with time, as your example human voice that varies over time in air pressure (or equivalently voltage), some vary with space, like image and some vary with both time and space ...

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There are various school of thoughts about this. A German guy named Klaus Genuit came up with a parametric model of HRTFs that's based on the actual physcial geometry of head, shoulder, pinna, etc. He actually now runs a company that craeates binaural products and measurement gear, see for example http://www.head-acoustics.de/downloads/publications/...

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You want something that has lots of features in the spectral domain and in the time domain. However you have to be careful what other content or meaning you would like to convey or not. Spectra conveys pitch and/or coloration and timbre. Time transients convey rhythms and/or patterns. For example you could do band limited noise burst with different center ...

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If it is the repetitive pattern, you may observe some strong magnitude at some position in frequency domain. Use imagesc(abs(fftshift(fft2(img)))), you probably would see: Those small white spots in the image correspond to the repetitive texture that is superimposed on the image. You may then use ordfilt2(F,roi^2,ones(roi)) to find out the roi * roi local ...

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To get a panning effect I know that there is a need for a Circular Matrix. The angle is can be set as a default or I can let the user to select the angle. How do you know this? Can you cite a source? Audio Panning is typically done with one input and two outputs. It's either done with constant energy or constant amplitude pan. Constant energy is similar to ...

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These terms exist mainly for historical reasons. In signal processing the signal is a one-dimensional function of time. So people talk about the time domain vs. the frequency domain. On the other hand, in image processing you are looking at a 2D function of $x$ and $y$, and there is no notion of time. Instead your are talking about spatial frequencies. ...

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The components are instantaneously uncorrelated if $$E[y_i(t_1) y_j(t_2)] = 0$$ for all $i \not= j$ and for all $t_1,t_2$ and when $i = j$ : $$E[y_i(t_1) y_i(t_2)] = 0$$ for all $t_1 \not= t_2$. The components are spatially uncorrelated if $$E[y_i(t) y_j(t)] = 0$$ for all $i \not= j$ (assuming a change in index corresponds to a spatial change). Note ...

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If I understand you correctly you want to smooth the data (Namely reduce "Noise") yet regular filters would ruin the data on discontinuities. What you need is an Edge Preserving Filter. You can try the Bilateral Filter or Anisotropic Filter. I have an advanced implementation of the Anisotropic Filter - Fast Anisotropic Smoothing of Multi Valued Images Using ...

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Please see this response. Radiant Flux should take care of the physical units. The PSD in the spatial domain would represent a "dominant" blob, whose shape is determined by the spectral content of the PSD, underlying the image. (Please note that the PSD is defined over the Fourier Transform of the autocorrelation of a signal). EDIT: So, your pixel size ...

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The spatial sampling frequency of a linear array with equidistant sensor (antenna) spacing on the $x$-axis is simply given by $$f_x=\frac{1}{d}\tag{1}$$ where $d$ is the distance between the sensors (cf. Eq. $(35)$ in the document you linked to). What is meant by the equation in your question is the spatial frequency of the incoming wave along the $x$-axis,...

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Say I have a 1D (spatial) signal (resolution = $1000$) which is zero everywhere except from $x=250$ to $750$, where it equals one. This is not "resolution". Resolution is 300 Dots Per Inch. In which case, we could say that the total physical length of your pulse is $\frac{500}{300} \approx 1.666$ inches (or any other unit of length). I ultimately want to ...

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Nothing is wrong, apart from assuming that the delayed $\cos$ extends to infinity both towards negative and positive time. In other words, the delayed version of the cosine starts 2msec after the non-delayed one. Therefore, before that time, there is supposed to be silence (and complete silence is not part of the cosine as we know it). Theoretically, you ...

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Try this DELAY = 0.5e-3; % half a millisecond for starters. t = [ 0 : 1 : 2000 ]'; % Time Samples, COLUMN vector f = 500; % Input Signal Frequency 500Hz fs = 44100; % Sampling Frequency x = cos(2 * pi * f / fs * t); % Generate Sine Wave y = cos(2 * pi * f * ( t/fs - DELAY)); % Generate Sine Wave Y = [x,y]; A bunch of problems with your code Your ...

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The short answer is yes(1), no(2). The quantification of the power spectrum is performed via the Fourier Transform over an image. An image is a matrix that describes the distribution of reflectance (or radiation more generally) across the field of view of the camera. It therefore has only a remote "relationship" with what is actually depicted in the image (...

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Your plot shows the magnitude of the array gain vs angle of arrival for the broadside beampattern. Before you can take meaning IFFT of this sequence, you need to follow the following steps. The data in the plot is the magnitude of the array gain, which is stripped of all phase information. For IFFT to give desired result, we need to restore the phase ...

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i do not have a reference for you, but you are sampling among every one of the dimensions and the Nyquist Criterion must be satisfied for each one. so fix $v$ to an arbitrary value $v_0$, then check if $\tilde{f}(u,v_0)$ is bandlimited some bandlimit, we'll call $B_u$. find the max value of $B_u$ for all possible $v_0$, double that and that is the minimum ...

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Try a Median Filter with a large enough sliding window, to pull the outliers (discontinuous jumps) back within tolerance.

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In the end I managed to do this myself, working in collaboration with http://ir-ltd.net/ to get the scan of my head, Blender to refine the mesh and position a cloud of microphone points, http://www.waveller.com/Waveller_Cloud/ to compute the frequency responses for these points, and finally some Python/NumPy scripting to convert these into impulse responses. ...

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