Questions tagged [signal-processing]
Numerical analysis of a digitized signal
302
questions
1
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0answers
21 views
Does blind source separation (ICA) work if channels of mixture are observed asynchronously?
Does Independent Component Analysis (ICA - fastICA, SOBI, etc.) work reliably when applied to a multidimensional mixture (observation) $X = (X^1, \cdots, X^d)$ if the different channels $X^i$ of the ...
1
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0answers
29 views
The length of spectral density is longer than the data using spectrum() in R
I'm using spectrum(method = "pgram") in R to calculate the spectral density in my time series.
spectrum() returns the spectral density for each frequency(from 1/n, 2/n to 1/2, n is the time ...
0
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0answers
7 views
Why the baseline algorithm irls from the R baseline::baseline() function can generate negative values given a positive signal in input?
I am running the baseline function on the following signal in order to find peaks. Why is the computed baseline negative in some regions?
0
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0answers
6 views
Methods of analysing sensitivity of time-series signal data
I am trying to analyze the sensitivity of the data I got from a power plant model. Assuming I give step signal to different input signals(a,b,c.....), then I got a response of the output power(pa,pb,...
0
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0answers
24 views
Measure prosodic similarity using deep learning
I have a dataset of 12,000 audio recordings of nonnative learners imitating the prosody of native speakers (300 samples for each native speaker utterance). All the nonnative learners' attempts were ...
1
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0answers
38 views
Singular spectrum analysis and their "eigentriplets"
I am struggling to understand why eigentriplets arise when decomposing a signal by using singular spectrum analysis (SSA).
The term eigentriples refers to the components of a singular value ...
0
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0answers
13 views
How to compare/ quantify how similarity between wave patterns
I have an idealized wave pattern, and I'm trying to come up with a measure to compare how similar other wave patterns are to it. I'm more concerned with the overall shape and timing of a wave as ...
1
vote
1answer
24 views
Estimate the Image Using Multi Many Realizations of Its Convolution with a Known Filters Using Wiener Filter
Suppose we have a corrupted image $Y = H*X + \epsilon$ that is formed by taking an image $X$, convolving it with a point-spread function $H$, and adding gaussian noise $\epsilon$. Then we know that ...
0
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0answers
46 views
Calculate similarity between two time series using discrete wavelet transform cofficients
I am new to the field of signal processing but I have read that DWT can be used to find similarity between two time series, I am curious as to what kind of similarity measure do we use once we have ...
1
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0answers
16 views
Normalization, centering and PCA [duplicate]
I have a feature matrix composed of frequency responses (in dB) from individual acoustic events. Frequencies in the columns, events in the rows and the matrix is the response
The responses decrease ...
0
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0answers
19 views
I'm confused with cross correlation and covariance of signals
I've heard that correlation is just a normalized covariance buy they can be treated the same. I'm a bit confused about it-
I usually calculate cross correlation between two signals just like here in ...
2
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0answers
40 views
How to estimate a parameter from a unknown model as follow?
Recently I have met a question. I have derived a important indicator in my research on radar signal processing. The indicator $y$ can be calculated by another measured value $x$, and their ...
0
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0answers
32 views
How to Add two white random gaussian noises from different noise sources?
I have generated two different white gaussian random noises in MATLAB using two different seeds. For example:
Asn1 = sqrt(noisepow1/2)* (randn(size(As))+1i*(randn(size(As))));
Asn2 = sqrt(...
1
vote
0answers
41 views
Fitting sums of Gaussian-like density functions
I acknowledge that similar question has been asked a couple years ago, yet it still seems unresolved. Ignoring the domain knowledge, the statistical problem behind is to fit a non-linear regression ...
1
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0answers
36 views
Fitting data to sums of squares of sinusoids
I am given some time series data $(t, y(t))$ sampled at regular intervals $t=0,s,2s,3s,\ldots,1$ for some step size $s$, obtained from a function
$$
y(t) = y(t, \vec A, \vec \delta) = \sum_{i=1}^N ...
0
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0answers
19 views
Unable to interpret p statistics for periodicity testing of signals [duplicate]
The meaning of P value is probability which should be number between 0 (the event never occurs) and 1 (the event occurs always). significance testing for periodicity using Matlab gives a documentation ...
1
vote
0answers
34 views
Detect regularity in arrival times
I am working with series of arrival times. My typical dataset is made of 20-100 samples.
I would like to detect regularity in the arrival time.
By regularity, I mean that the inter-arrival times may ...
0
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0answers
18 views
Is it a good practice to pad signal before feature extraction?
I have a question for you - is padding, before feature extraction with VGGish, a good practice? Our padding technique is to find the longest signal (which is loaded .wav signal) and then in every ...
0
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0answers
31 views
Kalman gain without noise
I have the Kalman gain defined as $K_k=P_{k,k-1}H_k^TS_k^{-1}$ with $S_k=H_k P_{k,k-1}H_k^T+R_k$. Commonly, in the univariate case, this is written as $K_k=\frac{\sigma^2_{process}}{\sigma^2_{process}+...
0
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0answers
11 views
Choose the right Sigma for Gaussian low-pass filter
I have the following problem:
I have a time series with counted data. I now want to smooth it using a Gaussian low-pass filter.
Is there a method to determine the sigma value? The window should have a ...
1
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0answers
31 views
Does the convergence rate never increase of a Stationary Ergodic Random Processes under sub-sampling?
Summarize the problem
Given A Stationary Random Processes (strict sense) $X_i$ I define two Stationary Ergodic Random Processes by
$$
\bar{X}_n = \frac{1}{n} \sum_{i=0}^{n-1} X_i \ \ \text{and} \ \ \...
0
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1answer
83 views
how to make Kalman filter results equivalent to linear regression? [duplicate]
Statistics gurus,
Kalman filter appears to be a powerful estimator for linear problems. I understand one can tune the performance by adjusting parameters like process noise and measurement noise. Is ...
0
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0answers
15 views
Generation of multiple arrays or vectors with specific correlation among it?
I shall try to elaborate my question as much as possible because I tried multiple things but I am not available to find a possible solution.
Problem Statement :
I have an impulse response, say a ...
0
votes
0answers
27 views
Correlation estimation by the Blackman-Tukey method
Problem statement: Assume that the spectral estimation for an unknown signal is given by the Blackman-Tukey method as follows:
$$ S_x(\omega) = 5+8\cos(\omega)-6\cos(2\omega)+2\cos(3\omega).$$
Assume ...
0
votes
1answer
50 views
Finding autocorrelation coefficients given PSD values at 2 frequencies
Assuming that $S_X(w)$ denotes powers spectral density function at
frequency $w$, we are given
$$S_X\left(\frac{\pi}{4}\right)=10+3\sqrt{2},\quad
S_X\left(\frac{\pi}{6}\right)=11+3\sqrt{3}.$$ We also ...
0
votes
0answers
18 views
Is the t-test valid for the real measurement?
I'm working with a real signal measured by a machine. I've compute the mean and standard deviation, it's nearly the same. However the Allan deviation is not good enough.
I want to estimate the noise ...
1
vote
1answer
28 views
Step change detection in signal by convolving with step vector
I am facing the following problem in signal processing and I have run into a wall. I am trying to detect abrupt changes (step changes) in a constantly decreasing signal by convoluting the signal with ...
1
vote
0answers
20 views
Seeking an algorithm to turn a continues signal into binary
I had a nice project in mind, which I will probably not going to do because of a lack of time, but I had some theoretical problem I faced there, which still bother me and might be interesting for you ...
0
votes
0answers
32 views
Spectral decomposition of the DC part of the signal
The title of the question might be misleading, but it shows how clueless I am about this problem.
I have a time series of some quantity $X(t)$. I can calculate autocorrelation of this quantity $Y(\tau)...
0
votes
0answers
30 views
Calculate mean and variance of a timed signal
I have a function rapresenting a signal, where x-axis are times and y-axis are signal volts. Signal seems to be sampled at 200Hz, even if for some "times" it have 199 samples and not 200 as ...
0
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0answers
20 views
Can I decompose a signal in interval T between symmetric and anti-symmetric signals?
I have computed many samples of a signal $y_n[t]; t\in \{-T/2, T/2\}$ for $n=1,\ldots,N_{\text{samples}}$. I want to model this with an even function $f(t;\vec{\theta}) = f(-t; \vec{\theta})$ where $\...
1
vote
0answers
52 views
Uncertainty principle in probability theory
In probability theory, there is the covariance inequality
$$\operatorname{Var}(Y) \geq \frac{\operatorname{Cov}(Y,X)^{2}}{\operatorname {Var} (X)}.$$
In signal processing, there is a similar ...
0
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0answers
44 views
Help with time series comparisons using periodograms
I have a dataset consisting of time series signals of different lengths obtained from different groups of patients. I am trying to understand the commonalities of the time series of each group. ...
0
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0answers
26 views
standard deviation of two constant noised signals related through interpolation
Let us say say we have a noised constant signal and want to evaluate the standard deviation (std) of the noise. We calculate the std of the said noised signal and call it σ1. Now we process the signal,...
3
votes
0answers
67 views
ICA: a question about the non-gaussian requirement
I'm new in the ICA processing and I'm trying to understand the non-gaussian requirement. I read that the problem is that, if the composed data is $\mathbf{x}=\mathbf{As}$ with $\mathbf{A}$ (unknown) ...
0
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0answers
7 views
why is the signal divided into epochs in EEG classification?
I'm new in EEG signal classification. Studying the literature on this topic, I wonder why the EEG signals are divided in epochs, so, instead of classifying the whole signal all at once, we usually ...
0
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0answers
18 views
CTC Speech Recognition Model giving absurd results on actual recording
I have trained a speech recognition model which uses CTCLoss and is inspired from
https://www.assemblyai.com/blog/end-to-end-speech-recognition-pytorch
I trained it on the Librispeech Dataset (train-...
3
votes
1answer
48 views
Signal processing: How to patch over dip in signal using R?
I am a biologist starting to use R to analyze my data.
Could anybody please help me solve my problem I encountered when working in signal processing in R?
Problem
I have a recording of a signal in a ...
0
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0answers
19 views
How can I "remove" variability in my data that is due to periodic signals, such as Temperature, RH and Solar radiation?
I have a measured signal that I know is affected by some periodic signals, such as Temperature, RH and Solar radiation.
Is there a way that I can "remove" their influence from my measured ...
1
vote
1answer
50 views
What is a suitable way to reveal correlation between these two signals?
I have two time-domain data signals which look like the following:
I know that variations in $x$ are able to induce variations in signal $y$, and would like to be able to show that "yes, x is ...
1
vote
0answers
60 views
Are the following model assumptions on a data stream too restrictive?
Suppose that you were to model a "generic" continuous-time real-world data signal $X$ taking values in a bounded continuum $K\subset\mathbb{R}^d$ (e.g. the body temperature of a patient or ...
1
vote
0answers
35 views
What is the meaning of noise in a dataset with no dependant variable?
My understanding of noise & signal comes from the context of bias-variance tradeoff in supervised methods.
But given a dataset with no dependant variable, how do you define noise? & how do you ...
2
votes
0answers
41 views
Does it make more logical sense to model the discrete FFT output as a categorical variable or a numerical variable?
I am training a time-series data classifier and some of my features are the output of CT FFT. The results are of course discrete frequencies. I understand that they are in numerical order and higher ...
5
votes
2answers
106 views
Seeking recommended literature search terms for a solution to a specific kind of data structure?
Hopefully this isn't considered too off-topic. I'm working in industry these days and came up with a solution to an analysis problem we'd been facing. I'd like to get a sense as to whether said ...
1
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0answers
38 views
What is the similarity and difference between signal recovery and parameter estimation?
As per inferential approach both are estimation problem. But, in signal recovery, we estimate our input signal from the measured (noisy or noise free) observations. And, in parameter estimation, we ...
1
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0answers
22 views
What are the parameters in signal recovery? Whether source of these parameters are the sampling property of impulse response?
I was reading the following book: Juditsky, Anatoli, and Arkadi Nemirovski. Statistical Inference via Convex Optimization. Vol. 69. Princeton University Press, 2020. Here, I could not visualize the ...
0
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0answers
16 views
Encoding a time series with varying time differences as an image
There are methods to encode a time series into an 'image' i.e a matrix of scalar values. Some methods include recurrence plots, gramian angular field and markov transition field. Most methods assume ...
2
votes
1answer
81 views
ML model for Signal Decomposition [closed]
So recently I got a task which can be summarized as follows:
Suppose we have 3 functions f1, f2, f3 and a certain combination of the functions gives us ...
1
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0answers
41 views
How to identify the frequencies of periodic peak signals in a noisy time series? (with R)
Suppose to have two time series with peak signals at different frequencies, like these two:
...
0
votes
1answer
1k views
How should I approach (in Python) to detect the change points in following time-series signal? [closed]
I want to extract different signals present in this image. To do so, I want to find the boundaries of change point at 2.429 GHz, 2.444 GHz, and so on. Note: These numbers are observed visually and ...
