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First of all, I wouldn't worry too much about the speaker response since it is relatively flat and the microphone has a much bigger roll-off. Since you've captured the frequency response using sweep, why not to skip the whole part of designing the filter that mimics the frequency response and use the original impulse response? I don't know what kind of ...

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In a way, you have answered this already with: FastICA suppose that we have as many sources as channels, but does not check it in any way. If I am going to run the algorithm on my data, it would extract two sources and a 2x2 mixing matrix. ICA will indeed provide a separation, along the lines of section 3 ("What is independence?") which would also ...

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Is this a signal generate from a raw mp3 file or it is generated from a Matlab-decoded-mp3 file The latter. $audioread()$ reads the raw MP3 bit stream, decodes it, and returns the decoded audio data as PCM data. If you want the raw bit stream use $fread()$ instead.

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There could be a number of approaches for this task, depending on the number of channels (microphones) in your input and structure of available data. Given a labeled data set from a previously known set of rooms, you can train a neural network (NN) to classify in which one of them a new sample was recorded. A second approach would be to estimate the ...

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Most Audiophile stuff*) has a credibility on par with homeopathy. It might work - provided that some sales-oriented cable manufacturer single handedly revolutionized science. But in all likelihood it is most about clever marketing and gullible buyers. Fortunately there are test protocols to prove that sensory perceptions are different. Unfortunately, the ...

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dBFS is a digital signal measurement, relative to full-scale. dBSPL is a sound pressure level measurement, relative to 20 μPa RMS air pressure. dB(A) is shorthand for "dBSPL A-weighted", which is the same dBSPL measurement after applying an A-weighting filter. You're going to have to thoroughly understand these concepts before you can convert between them. ...

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When you add (wave mix) multiple raw audio wave sources, you must first make sure that they use the same sampling rate and then same numerical format, so that addition can be realized. So, if they are not at the same sampling rate already, then you have to resample them to the same rate. And also make sure (by type conversions) that all of them to use the ...

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I believe the answer to your question is Short-time Fourier Transform or Short-term Fourier Transform. There is the wikipedia article. I tried to show the essential math in this answer. Both the Phase-Vocoder and Sinusoidal Modeling are done with the STFT of some form or another. The two methods sorta merge in concept at the STFT level. Remember that ...

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Hydrophones have a weird way of specifying sensitivity. $1V/ \mu Pa$ simply means $10^6 V/Pa$ which is frankly an idiotic unit. You scale this by -200dB and you get $S = 10^{-4} V/Pa$ or $S = 0.1mV/Pa$ which is a much more reasonable representation. This has NOTHING to do with dBSPL which is referenced to the human threshold of hearing. There is no ...

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The basic idea is called - Music Finger Printing. Searching for it will yield many results. I'm attaching few good ones I found: How Does Shazam Work? How Does Shazam Work? Music Recognition Algorithms, Fingerprinting and Processing. Creating Your Own Shazam (Identify Songs) with Python Through Audio Fingerprinting (The YouTube Video). Audio ...

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First, the original $x$ and reconstructed $\hat{x}$ signals have a peak amplitude around $0.35$. Their difference peaks above $0.40$. That happens between two time-shifted like-alike signals. Second, the difference seems to relate to the amplitude. Processing maybe be non-linear, and it could be interesting to look at relative differences like \$2(x -\hat{x}...

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To make it sound natural you typically introduce some small random pitch, speed & gain modulations. That's not a trivial amount of work, especially if you want something that sounds natural and good and maintains the original phrasing. This is a pretty common plug-in in audio processing. It's typically called "vocal doubler" or "voice doubler" and ...

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A better approach for use in the presence of noise and distortion is to use the Wiener-Hopf equations which will provide a least-mean-square solution of the effective "channel" between the microphones. The group delay can be determined using scipy.signal.group_delay. Further details on this approach specific to microphone captures including Matlab/Octave ...

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Actually, as mention by @applesoup your question is not clear but as far as I understood you can repeat the signal using numpy.matlib.repmat(a, m, n) Repeat a 0-D to a 2-D array or matrix MxN times. Parameters: a : array_like The array or matrix to be repeated. m, n : int The number of times a is repeated along the first and second axes. ...

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