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New answers tagged audio

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You want my number? I always see you on tic toc and i love you so much!

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Hello check this source To begin with, let’s remember what the fundamental frequency is and in what tasks it may be needed. The fundamental frequency, which is also referred to as F0, is the vibration frequency of the ligaments when pronouncing voiced sounds. When pronouncing unvoiced sounds, for example, by whispering or uttering hissing and ...

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Audacity is trying the "help" you by keeping your samples within the displayed range of -1.0 to +1.0. Instead of clipping, Audacity is wrapping the out-of-range values completely around to a value of the opposite sign, which is typical of 2's complement arithmetic without overflow checks turned on. (perhaps scaled from the displayed floating point range ...

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If you’re looking for modeling the amplifier itself, convolution will not provide a complete model for the internal processes. However, convolution is the basis for a number of cab modeling products. I have a line 6 helix that I use frequently. A dry guitar doesn’t sound great. A dry guitar through an amp model sounds bad. A dry guitar through an amp and ...

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When talking about modeling, there are two things that usually get modeled: 1. the guitar amp, and 2. the speaker cabinet. Only the latter is modeled by an impulse response, which means that the cabinet is simply represented by an LTI system and implemented by convolution. This is of course an approximation but it works fairly well. You can find a lot of ...

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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; if you scale the input by a factor, the output is scaled by the same factor. Linear, time-invariant, so to speak. LTI systems can be applied to time-domain ...

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Your samples have a finite bitwidth. For example, assume your samples have 16 bit width; if you increase their amplitude beyond what these 16 bit can represent, you end up with an overflow. In case your audio samples being signed integers, that overflow typically first manifests as a sign inversion – exactly what you're seeing here. (That's because only the ...

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We discussed this phenomenon on the music-dsp mailing list in May 2014. For long enough a period, the audible repetition is not directly about the frequency spectrum but that instances of white noise are not white but usually contain distinct features or patterns that can be learned and then recognized in the later periods. In some instances, there will be ...

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@Marcus I took your advice based on the correlation. So i modified the Y segments to generate different white noise at each instance. Here is the MATLAB code for the same. clc;clear all;close all; block_len = 4096; YY = wgn(block_len, 1, 50, 10, 'dBm', 'real'); XY = wgn(block_len, 1, 50, 10, 'dBm', 'real'); ZY = wgn(block_len, 1, 50, 10, 'dBm', 'real'); ...

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If you were to assemble a very large number of these segments and then take the FFT of the entire signal, you would see that you have a series of spectral sticks every FS/4096 hz, with nothing in between, because the signal you have made by concatenation is periodic. However your ear does not have an infinite analysis window, so once your block length is ...

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Rather than generating a longer block of data, i would like to make a longer file by concatenating the block of 4096 samples. Bad idea. That means your noise becomes perfectly correlated with a period of 4096 samples, and that's definitely not white noise anymore, and you'll stand a realistic chance of noticing that audibly; depending on the sampling rate, ...

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For non-periodic signals (size(y) -1) has to be substracted from the index of R_xy to get the actual lag. N = size(x) + size(y) - 1; lags = [0, N] - (size(y) - 1);

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in order to estimate the period of a quasiperiodic signal, normally you need an entire period plus a little more. you need to compare a little snippet of the waveform with its counterpart in the adjacent period. that's the fastest i expect a pitch detector to come up with anything.

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To answer my own question here for anyone who would like to do something similar, I ended up designing an analog circuit instead of DSP. I used a signal multiplier chip called AD633 to multiply the two frequencies. Then I use a bandpass filter to extract the sum frequency.

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This is a beautiful way of noise/interference cancellation technique, please go through the link that I am sharing here. Certainly, you may find a way to implement the concept. https://drive.google.com/file/d/0B2bUtLEhrWp8Wi1JZzdub0U2Wm9JWlZEX290cHByZi1ES3FZ/view?usp=sharing

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As Stanley Pawlukiewicz said: even under ideal circumstance, you can gain 3 dB of SNR per doubling of recordings. I.e., to increase SNR by, say, 15 dB, you'd need to average $$2^{\frac{15}{3}} = 2^{5} = 32$$ recordings. That alone shows that the whole thing isn't really practical: it just doesn't do much unless you use a crazy-high number of recordings. “...

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A "pure" signal, No. A less noisy signal? Possible? Yes but there are several complications that may make this impractical. You basically need to align the 2 recordings and then add them. You might gain 3dB in SNR. but The paths from the source to the 2 locations aren't the same, so they will differ to some extent, so the copies may not add ...

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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 increase the the SNR simply by averaging; that becomes pretty obvious when you consider the signal of interest to be correlated within your recording, whereas your ...

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