Questions tagged [signal-processing]

Numerical analysis of a digitized signal

Filter by
Sorted by
Tagged with
3
votes
1answer
33 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
votes
0answers
14 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
30 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
58 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
19 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 ...
1
vote
0answers
31 views

How to estimate an AR model using OLS

I am trying to estimate the coefficients of an AR(2) model of order 2, length 3 using least squares method. I have used the \ backslash operator in Matlab. Can ...
2
votes
0answers
31 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
100 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
vote
0answers
29 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
vote
0answers
19 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
votes
0answers
15 views

Smoothing methods for unevenly sampled data

I have categorical, time-series data distributed in space. It is very noisy, but over the whole series there are big shifts in distribution - my goal is to see how these shifts progressed in space. So,...
2
votes
0answers
17 views

Strange constraint in optimization problem

I am trying to solve the following QP : $$ \min_{x} \quad (1/2)\|y - Ax \|^2 _2 + \lambda \|Dx\|_1$$ where $y \in \mathbb{R}^n$, $A \in \mathbb{R}^{n\times m}$, $D \in \mathbb{R}^{l\times m}$. Assume :...
0
votes
0answers
10 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
57 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
vote
0answers
27 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
58 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 ...
0
votes
0answers
17 views

Similarity index between 2 unevenly sampled time series

Say I have 2 time series $s_1$ and $s_2$ with independent variable $x_1$, and dependent value $y$. These 2 series are not evenly sampled across $x_1$, or even sampled at the same rate. Now, I have ...
0
votes
0answers
25 views

Why do AR(1) times series generated by two methods look similar but have different variance estimate in Python

I come across one question when I use two ways to generate AR(1) sequences. By definition, AR(1) sequence is $x_t = \phi_1 x_{t-1} + \varepsilon_t,\quad \varepsilon_t\sim N(0, \sigma^2)$ I found ...
0
votes
0answers
12 views

Representing a time-series smoothed curve as a sinsoidal?

So attaches is an example of the kind of time series data I am working with. So far I have used Gaussian filters with sigma=3 and 6 to smooth the data, which has worked very well (especially sigma=3). ...
0
votes
0answers
9 views

Fourier transforms for noise reduction

Given a signal, which is regularly sampled over time and is noisy. The standard method is with a Fourier transform to reduce the noise and minimise the change to the signal. ...
1
vote
1answer
29 views

Periodogram explained

If I plot a periodogram of let's say sin(20x) + 2sin(80x) and it looks like this: What does it say, i.e., how do I interpret this periodogram? How could I compute ...
0
votes
0answers
17 views

speaker verification model trained on one dataset does not perform well on another

I am quite new to audio signal processing, more specifically speaking speaker verification. I have trained a CNN-based Siamese network to do speaker verification. The whole thing is trained with one ...
0
votes
0answers
16 views

Resampling Ground Truth - Manipulation?

A validation task requires comparing a timebound-signal (out of a system under test) of length $1\times m$ to be compared against a ground truth (GT; reference) timebound-signal of length $1\times n$, ...
0
votes
0answers
22 views

Is resampling multi-variate time series data a useful practice in increasing binary classifier accuracy?

Let $x$ be defined as a multi-variate time series with length 30 seconds a sampling frequency $F_s = 60\text{ Hz}$ columns $\{C_1, C_2, C_3\}$ My first question is, in general, would resampling $x$ ...
0
votes
0answers
9 views

Are there any recursive online max/min filters for time-series

Are there any online recursive filters that can approximate local, time-varying minimum and maximum values of a time series?
0
votes
1answer
16 views

Fourier analysis to retrieve components of individual spectra

I have a basic, simple question, I am a physics student, and searching internet gives me a lot of signal processing theory but couldn't find this basic answer, which I plan to implement in my speech ...
0
votes
0answers
7 views

How to model noisy signals in time knowing some expected behavior?

First off, pardon me for the very informal language and the lack of demonstrative media. I'll try to add some as soon as possible. Imagine an 8bit grayscale image with a noisy background, two ...
1
vote
1answer
12 views

Different signal length for each batch

I wonder if it Is it possible to have a different signal length for each batch when training a model. Batch 1 : all signals of length 1000 Batch 2 : all signals of length 2000 Batch 3 : all signals ...
0
votes
1answer
50 views

the concatenation of bivariate iid

suppose that $X \sim N\left( {0,{\sigma ^2}{I_2}} \right)$ is a bivariate white noise, and the samples ${X_1}, \cdots ,{X_N}$ are drawn from it, if we define the new random variable $Y$ with its ...
0
votes
0answers
8 views

Feature extraction for exponentially damped signals

I am looking into exponentially damped signals where it is a stationary signal (after implementing the Adfuller statistical test) and I would like to look into how can I extract meaningful features ...
0
votes
0answers
33 views

How to reflect more global patterns in timeseries?

I have some signal data of a robot recorded in every minute each day. e.g., ...
0
votes
0answers
13 views

clustering in a histogram [duplicate]

I am using python/numpy to create a histogram as follows: ...
0
votes
0answers
16 views

How can i compute noise (1 sigma error) given a signal

I have a signal organized as an image, i.e. a matrix. Each "pixel" has an error $\sigma_{i,j}$. Simplifying, let's assume that the error is the same for all the involved "pixels". How can I compute ...
0
votes
0answers
6 views

How to choose from a group of parameters for every single estimation?

I have done a series of SNR-estimation with the ground-truth SNR from 0 to 15 dB at a step of 0.1 dB, 1000 samples each time. So there are 151 distributions and they all follow ExtremeValue ...
0
votes
0answers
11 views

compensation filter for effect of dependent variable

In my dataset, I have two variables A, B. Behavior of B depends on A. A varies from 0 to 100 and a transient event results in a spike in variable B, that looks like this: It's effect lasts for a ...
0
votes
0answers
12 views

Spectra data augmentation

I am working on spectra dataset (magnetic resonance) and would like to perform data augmentation on top of that. I found this paper which doesn't seem to be clear (at least, to me) on what the exact ...
4
votes
0answers
77 views

Features for binary time-series event prediction

This question is somewhat inspired by the answer to Features for time series classification. The difference to that question is that I have a dataset with multi-dimensional time-series where I have ...
1
vote
1answer
170 views

Cholesky decomposition or alternative for negatively correlated data simulations

I want to generate some signals that have a correlation distribution around a specific pre-defined correlation value (i.e., the distribution of the values of their correlation matrix is around a ...
0
votes
0answers
12 views

Discrete Fourier transform (DFT) of 2 signals using a single DFT

Given two different(x,y) and independent signals, can one find dft of both of them using a single dft chip and using that that chip only once. I tried finding dft of x+jy but was not able to find ...
0
votes
0answers
20 views

In covariance function of white Gaussian noise, where does the delta function come from?

In probability theory, for the covariance of white Gaussian noise, where does the delta function come from and how do you prove it?
2
votes
1answer
29 views

AR process stationarity

For $X[n] =aX[n-1]+W[n]$ When $W[n]$ is iid. One can say that $X[n]$ is the output of $W[n]$ thrown into an LTI system. So how can it be that $X[n]$ is not necessarily WSS, if we know that a WSS ...
0
votes
0answers
27 views

Math for Gaussian noise on top of another Gaussian noise

I have worked on this project for a while and I have some results. However, I want to communicate in my paper the mathematics involved. When an image is introduced with 11 standard deviations, this ...
3
votes
1answer
90 views

Formulas for higher order cumulants

I want to calculate higher-order joint cumulants for 2 variables. I calculated the higher order single-variable and bivariate moments numerically. Now I need to combine them into cumulants (upto the ...
2
votes
0answers
30 views

Finding exponential decay in noisy vibration signal

I have to analyse vibrational signals for which the general assumption is that there is one dominant excitation and an exponential decay in amplitude thereafter. I have created smoothened envelopes ...
1
vote
0answers
62 views

What is the laplace transform of the below given PDF?

Really am interesting to know more about statistical properties of the following PDF , of the Random variable $z$: $$F(\sigma,\mu,z)= \frac{(z-\sigma )^2 \exp \left(-\frac{(z-\sigma )^2 \sqrt{\left(...
0
votes
0answers
11 views

Can a DNN learn the GCC-PHAT algorithm?

How would you create a DNN (and its training dataset) such that it is able to copy the GCC-PHAT algorithm?
0
votes
0answers
12 views

Signal/Wavelet Clustering

Problem Setting In an experiment: I have 3 signal sources and 10 sensors each generating wavelets as time goes by. The distances from each source to sensors change from experiment to experiment. ...
0
votes
0answers
10 views

Performance measure for estimation for acoustic impulse response

In searching for a performance measure for assert the estimation quality of acoustic impulse responses. Ideal Acoustic Impulse Responses (AIRs) are usually modelled as trains of impulses: $$ h(t) = \...
0
votes
0answers
16 views

Signal Embeddings using the skip-gram or CBOW model

So my work involves looking at a bunch of waveforms in the context of classifying events. I often am looking for new ways to represent my waveforms, and in my searching, I came across audio embeddings ...
1
vote
3answers
219 views

why is an MA process equivalent to a FIR filter?

John Cook claims that a FIR filter is equivalent to an MA process. But FIR filter is just a function of the previous inputs: $y_t = \phi(B)x_t$ and an MA process is a function of the previous ...

1
2 3 4 5 6