12
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 ...
11
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 ...
9
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 ...
6
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 ...
5
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 ...
5
The consumption time and transmission time is identical: One second of data is still one second of data regardless of sampling time.
The greatest common divisor between the two rates is 300, thus to resample this exactly from 44.1KHz to 48KHz you would need to use the ratio $160/147$ (and the inverse for the other direction):
$147$ is factored into $3, 7^...
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
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 ...
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 ...
3
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 ...
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
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 ...
3
Frequency is mathematically defined as the number of cycles per second. So it is a more strict word mathematically. It is represented numerically by the unit called Hertz.
$f=1/T$, where $T$ represents the one-period length of a waveform. This makes frequency quantifiable.
Pitch on the other hand, is a perceptual characteristic of a sound frequency, so it'...
3
The usual answer is that that you must convert by integer ratios, therefore 44.1 kHz to 48 kHz requires integer up and down conversions. Since this has been repeated in DSP text books for at least the past 50 years, it's almost always the answer you'll get using a ratio of m/n, where m and n are integers. The typical way uses a windowed sinc function—sinc ...
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
Good question as you can actually undersample and oversample at the same time! See my "DSP Puzzle" question on that specifically here:
How do you simultaneously undersample and oversample?
To best explain undersampling and oversampling, it is worthwhile understanding the concept of "Nyquist Zones" first. This was explained in detail recently at this post:
...
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
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
"Sharp peaks" in the time domain translates to high frequency content. Your simple algorithm of min and max values would be improved by first passing your data through a high pass filter - otherwise slow (low frequency) rumbles could degrade your detection methods.
To take your solution to the next level, you should use an "onset detection" algorithm. These ...
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