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The order of the sound field indicates that there are constraints placed upon the sound field. In this context, the order equals that of the lowest-order Ambisonics multi-channel format that can describe the sound field. Ambisonics channels are weighted by directional patterns that are orthogonal spherical harmonics: Figure 1. Directional patterns of ...


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First, I do not find any information on what is the order of the sound field. Can anyone elaborate please? $Y_{lm}(\Omega)$ is the spherical harmonics with mode $m$, order $l$ and frequency $\Omega=(\theta,\phi)$ (dictating angles of arrival, ex: azimuth, spherical, elevation, etc.). According to [1], we have the following formula: $$Y_{lm}(\Omega) = \...


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You have a bunch of options here You can zero pad both impulse response and signal to 16k, FFT and multiply. That's very inefficient though. Keep in mind that the sum of length of filter and signal need to be equal or smaller then the FFT size, hence an 8k FFT doesn't work. This gets a little better if you can truncate the 8k impulse response down to 7.5k. ...


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First, I would check the positioning of sound level meter and microphone. If you are putting them close to the oscilloscope a few centimeters of displacement may change the measured value dramatically. (6dB/doubling of distance) Second, which microphone do you use? The link you provided leads to a preamplifier. The actual capsule can either be a free field ...


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what you are describing is equivalent to a man-in-the-middle network attack. https://en.m.wikipedia.org/wiki/Man-in-the-middle_attack . If the original file was watermarked, that would offer the possibility of some authentication. Unless the individual who edited the file was inept, I don’t believe that you can in general detect the difference.


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Sometimes people use time-frequency representations (TFRs) as a generalization of the spectrogram. This C code calculates some different TFRs of Cohen’s class: bilinear TFRs.


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so here's a short MATLAB script that will compute and draw a spectrogram. if ~exist('inputFile', 'var') inputFile = 'tom_hit.wav'; end if ~exist('frameLength', 'var') frameLength = 8192; end frameLength = 2.^(ceil(log2(frameLength) - 1e-10)); if ~exist('frameHop', 'var') frameHop = 256; end frameHop = 2.^(floor(log2(frameHop) + 1e-10)); ...


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Usually when talking about accuracy the classical Fourier Uncertainty principle https://en.wikipedia.org/wiki/Fourier_transform#Uncertainty_principle enters the conversation (I would skip the quantum mechanics stuff). Essentially you can have good resolution in frequency or good resolution in time but there is a tradeoff between the two. In order to go ...


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The ltfatpy 1.0.16 package is a partial Python port of the Large Time/Frequency Analysis Toolbox (LTFAT), a MATLAB®/Octave toolbox for working with time-frequency analysis and synthesis. Among linear an quadratic time-frequency, there is a large number of options for sharper analysis tools converting a 1D signal into 2D data. You can even get more ...


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A long enough waveform plot at a high enough resolution allows reproducing sound from an image. Old movie film projectors reproduced sound this way (converting optical density images into amplitudes).


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For real valued signals (i.e. contains only positive frequencies) the Wigner-Ville (WV) distribution is a popular method. Check out this page for more information. The WV method provides some better localization than your typical spectrogram is capable of. That webpage I linked has some great examples, namely the linear-frequency modulation "chirp" signal ...


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