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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 ...
10
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 audio signals are very "Fourier-y", to say, they assume that sound is composed of weighted harmonic oscillations, and bandlimited in its nature.
Neither linear ...
8
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 ...
8
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 ...
6
The fact that you are plotting the FFT of the whole audio clip makes me think that you are looking at the wrong class of solutions. From the waveforms of signal A and B, it is clear that your noise and signal are not stationary. If you want to use a frequency-domain noise reduction technique, this should be done, in your case, on shorter overlapping windows ...
5
My own interpretation:
Sound: a mechanical wave that propagates through the air or water.
Audio: sound in the 20 Hz to 20 kHz range; in other words, sound that is (at least in theory) audible to a large number of humans.
Voice: sound produced by the human vocal tract.
Speech: intelligible voice (i.e. not grunts or screams)
Tone: signal dominated by a ...
4
The problem is that the stft function is splitting the signal up into different windows. That means that the signal from time $n$ to $n+N_{w}-1$ is multiplied by
$$ n, n+1, n+2, \ldots, n+N_w-1$$
instead of
$$
0, 1, 2, \ldots, N_w-1
$$
which is causing the scaling problem.
If I apply the group delay calculation from this derivation, I get:
where the top ...
4
If the noise signal B is highly correlated with signal A (means related through linear filtering) than you can use an adaptive FIR filter to subtract out the noise from signal B and leave just C. If the noises are uncorrelated but just have the same spectrum and/or probability density function, spectral subtraction or Wiener filter (or a combination of both) ...
4
Audio signals
An audio special-purpose analog-to-digital converter (ADC) normally has an internal or external analog low-pass filter and samples the analog filtered signal at a multiple of the target sampling frequency. This high-rate digital signal is then low-pass filtered by a digital decimation filter and decimated to the final sampling frequency. If we ...
3
The trigonometric functions "do not know" what a Hertz is and they do not care either. The only thing they know is that a full circle is $2 \pi$ radians. Whether this circle concludes in days, hours, picoseconds or a slice of it represents the angle a force is applied to some lever, is immaterial.
$2 \pi \omega$ expressed in Hertz, denotes a rate. A rate of ...
3
If you have a reference signal you want to find in a different signal then your model matches almost perfectly (Up to the environment the signal to be found is in) to Matched Filter.
So basically you need to do cross correlation between the Test Signal and the Reference Signal.
Find the point of maximum correlation and create a cropping zone around it ...
3
Audio quality assessment is one of the most critical pieces of audio coding and enhancing applications. The task requires an accurate and objective (mathematical) modeling of human auditory system including its subjective virtues.
However, the task of subjective quality assessment is one of the most complex problems to be attacked on Earth. Currently all ...
3
Sorry MM, I'm agreeing with Havakok on this one: A time domain interpolation solution should do just as well, practically speaking, and be significantly cheaper in terms of computation. (Assuming most frequency content is a ways below Nyquist).
I would go with cubic interpolation so you don't have any "corners" at the original sample points, which are of ...
3
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.
“...
3
Now is there a general method to analyze and determine the exact function of that effect
No, there can't be. Effects can (and will be) arbitrary, non-linear, memory-affected mappings.
You will have to make a model of what the effect does and then look for an estimator for the parameters of that model.
For example, if you radically restrict yourselft to ...
2
In simple terms, Image shown here speaks for itself.
Before speaking about Fourier Transform black magic lets understand idea behind it. Work of the Mathematician Joseph Fourier demonstrated any arbitrary periodic (this is important) signal can be decomposed into bunch of sine-waves at with their corresponding amplitude and relative phases.
Essentially, ...
2
Neither. The basilar membrane in your ear performs a frequency-to-place transformation which is then picked up by an array of thousands of hair cells (cilia). Therefore there are thousands of heavily overlapped bands. The processing that follows is very complex and not completely understood. The shape of the effective band pass filter at each location on the ...
2
The LMS and many of the variants of Adaptive Filters (In the Linear System context) work in the following settings (Intuitive):
You have access to 2 signals.
One signal is the result of the other one when a Linear System is applied.
This sounds really limiting, yet in practice it is powerful and flexible.
In the settings you mentioned the most known and ...
2
When upsampling the number video frames to allow playing a video in slow motion, rather than for a frame rate increase for smoothness, you will likely need to modify the audio using a time-pitch stretching/shifting algorithm to stretch the audio out (increase the number of samples) for a longer play duration (to match the increase in slow motion video ...
2
Yes. If you buy at least 1000 units, each will cost you 120
2
Your $H^{-1}$ is effectively what is called a zero-forcing equalizer for the channel represented by $H$. That channel incorporates DAC, amplifier, speaker, your room, the microphone, amplifier, ADC and several filters necessary to fulfill sampling conditions.
Problem is that for any frequency where $H$ has a notch, $H^{-1}$ has to have a very strong ...
2
As you mentioned MFCC features are one of the best features to represent audio as it captures both the time and frequency variations in the audio clip.You can get more details about MFCCS features in the below link:
http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/
You can import ...
2
Under the assumption that your background noise is statistically independent of the signal you want to recover and that your second microphone picks up virtually nothing from the body noise, you have the following model for your measurements:
$$ \begin{eqnarray} M_1(t) &=& L\{N(t)\} + B(t) \\ M_2(t) &=& N(t) \end{eqnarray}$$
Here, $M_1,M_2$ ...
2
Most audio interface devices are built for the commercial electronics industry which produces devices to be used by people to listen to music, radio, TV etc; for multimedia reproduction purposes.
Therefore the commercial electronics standardisation organisations, suggest or enforce the use of a number of frequency weighting filters (A,B,C etc.) for getting ...
2
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 and quadratic time-frequency methods, there is a large number of options for sharper analysis tools converting a 1D signal into 2D data. You can even ...
2
I was going to post this as a comment, but then it became so long that I thought, this constitutes really as an answer in fact.
Your question reminds me of the lab-work in my MSc course on Adaptive Filters. We used "Wiener filter" to remove some unwanted background noise (wind and tire noise) from the recorded input to make the speech clearer. The Wiener ...
2
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 ...
2
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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