# Tag Info

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 ...

6

Comments provided here are in two broad categories: Presentation and Subject matter. The "Presentation" section is the easiest to address. There are some things that could be rephrased in terms of language use but these might be just personal preferences. The "Subject matter" section includes comments in methodology which could take more time to address. ...

6

This is has nothing to do with the noise removal process but with your encoding. In order to do any type of processing with an encoded audio file you need to Decode the input file to PCM Process PCM data Encode the PCM into the desired output format Please note that steps and 1 and 3 are completely independent. The decode is determined by the input file ...

4

The Wiener filter considers statistical behaviours of the noise and the signal, and thus, (theoretically) achieves optimum separation of them for a class of signals and systems, which is not the case for more classical approaches. Wiener filter frequency response is such that, at those frequencies where noise power is dominant (a.k.a. low SNR), the gain is ...

3

If you are working on chat application (presumably web), then I suggest to take a look at WebRTC. It offers a noise suppressor that works ok for speech. Another option would be to use part of the Speex, which also has a noise suppression module.

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

There are 2 properties which are important to know about your noise: Its Self Correlation What usually called Auto Correlation. This will tell you the relation between 2 noise samples which are apart (On time) from each other (I'm assuming the signal is stationary, namely its properties doesn't change over time). Best thing to hope for is White Noise. Its ...

3

Upsampling is useful for noise shaping because it gives you some space in the spectrum for you to shape or steer the noise into. Suppose your application space is audio. You need to sample at least 40 kHz (or 44.1 or 48 kHz). So suppose it was upsampled to 96 kHz, instead. In the bit-reduction operation (quantization) much or most of that quantization ...

2

I don't really find papers that discuss this difference. There are whole books on the subject: Robust Automatic Speech Recognition 1st Edition Do speech intelligibility and speech quality correlate with the accuracy of speech recognition systems? Usually no, usually noise reduction corrupts features in unpredictable way and reduces speech recognition ...

2

I would recommend checking out "Design of active noise control system with the TMS320 family" by Sen Kuo [[report link]]. TMS320 chips are specifically designed to perform various ANC tasks. In the report, they even provide code for programming LMS and FxLMS. In a lot of the literature on real-time ANC, the control system is implemented using these chip ...

2

As suggested by Matt L., total variation denoising is probably the tool of choice. If the staircase morphology can be trusted, I would suggest looking at the notion of piecewise constant approximation. It is often meant to approximate generic functions along the idea if Riemann sums. However, this is exactly your model here, so you can introduce jump ...

2

Linear filtering won't work here. If anything works at all then it might be something like total variation denoising, which is a non-linear technique for removing noise while preserving edges. This article is a very good read, and it also shows a Matlab implementation of the algorithm.

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I would equalize both channels. The most robust method is to cut both back to a lower bandwidth. Then you only need to equalize the gains. Assuming that your scanning by hand a 1-2 second sweep rate seems reasonable. Unless your imaging, you can narrow the sweep down to smaller distances when a signal needs investigation. A 10-50Hz bandwidth should ...

2

I believe you are running into stability issues in an output power control loop design. See below a diagram of similar power control loops that I have implemented, where for stability reasons any filtering in the loop is minimized and only done with the loop filter itself which is designed for stability. The noise you are trying to filter gets filtered by ...

2

Assuming that the sensors share the same characteristics, have the same timing (acceleration signals are aligned), the model with $y_1= x + n_1$ and $y_2= x + n_2$, $n_1$ and $n_2$ being uncorrelated noises of the same power, averaging them is a way to reduce the noise. The theory that asymptotically, averaging $N$ sensors reduce the variance by a factor of $... 2 As mentioned in the comment, I modified the code given here and was able to adapt the LMS filter with error tapering to zero. The only assumption I made is that (since I am not an audio expert and do not know how the channel from speaker to the microphone would look like), I assumed a 10 tap channel with only first 3 non-zero values (multi-path reflection ... 2 This appears similar to a classic least squared solution of an overdetermined equation that proceeds as follows: Starting with: $$\mathbf{x} = \mathbf{A} \mathbf{s}$$$\mathbf{A}$is not a square matrix if overdetermined (more equations than unknownns) so therefore an inverse does not exist. What you do then is multiply both sides by the transpose of$\...

2

Your model $\mathbf{x}_n = \mathbf{s}_n + \mathbf{w}_n$ seems too simplistic. It basically says that your output is just some input corrupted by noise. (Unless $\mathbf{s}_n$ is not really your input but some transformed version of it.) Usually, it's more complicated than that in physical systems. This is why a better model would be $\mathbf{x}= \mathbf{... 2 A remarkably terse specification of the instrumentation (sensors, probes) and techniques used to measure "the vibration of a structure" leaves open the issues of applicability of the Wiener filter vs "the other (more classic) filtering methods" to processing of "the-vibration-of-a-structure" data. Well established techniques are ... 2 Window-average filter is an example of lowpass filter. That means, if interesting frequencies in your signal are few times lower than noise, then yes, you can do that. If they are close or higher, then no. Use bandstop/highpass filter if your signal frequencies are much higher than noise, or adaptive filter like Recursive Least Squares instead, if your ... 2 The variance of the white noise will go down by$N$through averaging (or standard deviation will go down by$1/\sqrt{N}$, where the standard deviation as a magnitude quantity will be visually consistent with the "spread" of noise on the graphic the OP is looking at. Thus to make it visually reduce by a factor of 10, the OP would need to average ... 2 In the domain of noise control, the noise can generally be controlled in three differenct places: the noise source, during propagation, and the reciever. The third one is relatively cheap and easy to implement, such as the well-known ANC earphone. If you don't want to wear earphones, that's the target of local active noise control. This is very hard and the ... 1 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 1 Can any body tell how to remove it completely so that it is not hearable? As suggested already, the signal has harmonics. You can diminish it further by keep filtering the rest of the peaks of the spectrum with a notch filter, or using the noise reduction option in Audacity. The latter option is better when the interference is over a useful part of the ... 1 The simplest way I can think of to remove noise from this kind of recording would be to use the noise removal functions in Audacity (https://manual.audacityteam.org/man/noise_reduction.html). This allows you to select a section of noise with none of the target signal and get a noise profile. You then remove spectral content that fits with this profile, ... 1 The "lub dub" sound of a heart beat is primarily between about 30Hz and 40Hz. A steep band pass filter for about 20Hz to 50Hz followed by amplification should bring out the beats themselves. If you need other heart sounds, then it gets difficult. Heart murmurs are a kind of wide band swishing noise - difficult or impossible to isolate using filters. ... 1 I will contribute to this question from my area of experience, which is in speech enhancement. For noise suppression, you can use classical speech enhancement algorithms (spectral subtraction, wiener filtering, etc) but they mostly introduce artefacts in the enhanced speech. Additionally, there are also machine learning approaches which you can try. I am ... 1 First of all the Wiener filter does not remove the noise but reduce it for WSS signals. It does this based on the relationship between the power spectral density of the clean signal$x[n]$and imposed noise$v[n]$, both assumed as WSS. The frequency response of a noncausal IIR Wiener filter is given as$$H(\omega) = \frac{ P_x(\omega) }{ P_x(\omega) + ... 1 Plus is the simplest operation :$z = x+y$. Fourier is inherently linear, and good at addressing it. However, most processes and data combination are nonlinear, and they should be dealt with. The second simplest operation is multiplication. Homomorphic filtering deals with$z = x\times y\$. It is more complicated, especially because of zeroes. Logarithms ...

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