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Questions tagged [signal-processing]

Numerical analysis of a digitized signal

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Signal fusion between different sensors

I have a 30-year time series of variable (soil water content at three depths) constrained between maximum and minimum values. During this period, three different types of sensors were used to record ...
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1 vote
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How should I go about completely decorrelating a digital signal?

So I'm working on real time signal compression, and I need to come up with the best convolution to minimize the entropy of incoming data (which I will then compress), which I understand is achieved by ...
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Question About Bayesian stats( from a DSP estimation theory book)

from "Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory" It is a fundamental rule of estimation theory that the use of prior knowledge will lead to a more accurate ...
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Precision of estimates of lower bit error probabilities at higher SNR

For my university lab in wireless communications, I simulated a simple uncoded BPSK (binary phase shift keying) channel with AWGN (additive white gaussian noise) to estimate the BER (bit error rate) ...
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remove "non stable" occurences in a time series

I have a signal, corresponding to a time series, in which I want to identify "outliers", actually corresponding to samples for which the signal is not "sufficiently stable" (see ...
MysteryGuy's user avatar
5 votes
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Hypothesis testing for detecting a (damped) sinusoidal signal in noise

I have a signal in white noise that has the following form: \begin{equation} r[t_i] = A e^{-t_i/\tau} \sin{(\omega t_i + \phi)} + n[t_i] \end{equation} I would like to test whether the signal (1st ...
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Do you change the mean / standard deviation when calculating the unbiased normalised autocorrelation function?

I am trying to calculate the unbiased normalised autocorrelation function. I think this field is a little complicated as different sources appear to use different nomenclature to describe the same ...
Steven Thomas's user avatar
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Estimation of a generalized noncentral chi-square dsitribution

According to Mathai A, Provost S: Quadratic Forms in Random Variables: Theory and Applications, 1992, the quadratic form of normal variables has a generalized noncentral chi-square distribution (or ...
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What is the meaning of graph singal for graph constructed from correlation matrix?

In the highly cited paper "The Emerging Field of Signal Processing on Graphs", the authors defined graph singal for a graph of N vertices as a vector of length N, with each element of the ...
Patrick's user avatar
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The sampling frequency or sampling period for financial time series in doing DFT

The discrete Fourier transform (DFT) is widely utilized in computer engineering, and its formula is as follows: $$X(k)=\sum_{n=0}^{N-1}x[n]e^{-i\frac{2\pi}{N}nk},$$ where the result $X(k)$ refers to ...
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increasing the capacity of an autoencoder

I have an autoencoder model with 5 layers in encoding and 5 layers in the decoding section. I am using this model for signal processing the problem is that it is making the signal way more smooth that ...
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Golf swing segmentation from time-series data

I need to identify golf swings in a set of time-series motion capture data. The following illustrates the captured Y-values (in video pixels) of a person's left hand over a period of about 10 seconds, ...
Hundley's user avatar
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Trained network always predicts zero [duplicate]

I have an encoder model and I'm training it with a dataset of signals with size (500,1). The data set is normalized and then used to train the model but the problem is that after the model is trained, ...
rrSep's user avatar
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Spiked tensor decomposition vs canonical polyadic decomposition

What are the similarities and differences between Spiked tensor decomposition and canonical polyadic (CP) decomposition? My understanding is that CP decomposition aims to find a low-rank approximation ...
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How to dectect sudden change in signal frequency? (analysis of EEG signals)

I am trying to analyze data from EEG electrodes to understand how brain activity changes in different coginitive states (for simplicity, assume that there are only 2 states: baseline (A) and chanelled ...
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Finding the best number of clusters including only one cluster (so no clustering)

Suppose you have a number of small pieces of signal from a sensor. These snippets are found earlier using signal detection. These snippets can be real events or noise. I now want to cluster these ...
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Identifying stress in financial stress index constructed using PCA using Hodrick-Prescott filter

I am reading: https://www.econstor.eu/bitstream/10419/128519/1/ewp-356.pdf The footnote on page 19 says: The trend was derived using the Hodrick-Prescott method where the smoothing parameter λ is set ...
user2338823's user avatar
2 votes
1 answer
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Minuscule data for classification problem

I am working on a frog classification problem where I have 15 unique species and 2 recordings per species. Would it be a valid experiment if I were to have my training set of the 15 unique species ...
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Calculate length of a signal

I want to calculate the length of a signal. By length I don't mean the number of points, but the actual distance from point to point. Imagine that a person would walk along the signal, what would be ...
manufacturing_analytics123's user avatar
2 votes
1 answer
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A statistic that describes variation in a stochastic process

Lets suppose we have two realizations from two stochastic processes. If we look at the values at y-axis as samples, both of these realizations have the same variance, but the second one is clearly ...
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Relationship between prediction model accuracy and measurement rate

I have a 2D signal that I can measure, with a Nyquist frequency of $f_{s}$ Hz. I also have a prediction model that tries to predict a few seconds ahead what the signal value will be. I have a choice ...
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Gaussian white noise model in application

I am interested in applications (to data) of non-parametric statistics, and my question concerned the Gaussian white noise model defined by, $$ X_{t_1, \ldots, t_d}=f\left(t_1, \ldots, t_d\right) d ...
BabaUtah's user avatar
3 votes
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Why do popular ML and statistical packages simply ignore classical estimation and detection algorithms for statistical signal processing? [closed]

For those who had a hard time to study and understand classical estimation and detection algorithms, and unfortunately realized that these algorithms are simply ignored by many packages that have the ...
Rubem Pacelli's user avatar
2 votes
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Calculating convolution in R [closed]

I am struggling to get the correct answer for the simple calculation of convolution in R. The convolution of $f(t) = e^{-t}$ and $g(t) = \sin(t)$ is: $$ (f * g)(t) = 1/2 \left( e^{-t} + \sin(t) - \cos(...
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Fused lasso for image denonising

For a given data $y_{i}$, with $i=1, \dots, n$, we consider the following signal approximation: $$ \hat{y} = \arg \min_{w}\sum_{i=1}^{n}(y_{i}-w_{i})^{2} + \lambda \sum_{(i,j)\in E}|w_{i} - w_{j}|, $$ ...
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What are the modes of a dictionary / transform basis?

So, I'm reading Steven Brunton's book, "Data Driven Science & Engineering", and I'm trying to understand what he means by mode in this following excerpt: Most natural signals, such as ...
Nyquist-er's user avatar
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1 answer
435 views

How do we relate RMS and standard deviation for continuous signals?

Because the discrete formula for RMS, $\displaystyle X_{RMS}=\sqrt{{1 \over N}(x[1]^2+x[2]^2+...+x[N]^2)}$, is almost the same as the formula for standard deviation (assuming mean zero), except for a ...
Homero Esmeraldo's user avatar
4 votes
3 answers
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Should a CNN generalize to arbitrary positions in the data?

I have trained a CNN on one dimensional data that is the power spectral density (PSD) of a $N$ different classes of signals ($N=4$). Each of the $N$ signals has a different spectral shape (not shown ...
BigBrownBear00's user avatar
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Effective way to down sample or up sample signals without losing information?

I have an edf data here that I got from this website. The data was supposed to be fed into an ML model. The data is taken from a sleep study (polysomnography). However, the data for some of the ...
JOHN EDWARD BINAY's user avatar
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Can you decompose a wave approximately?

I have data which looks like composition of sine waves. I need to decompose it to fewest possible sine waves that would give me tolerable error. The picture is of a half-period. Each half-period ...
Boppity Bop's user avatar
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How to deal with imputation of large continuous intervals of missing data in time series?

Imagine you have a dataframe of historical sensory data measurements, such as temperature and energy consumption, recorded every 15 minutes for 2 years. What would be the most appropriate way to deal ...
9879ypxkj's user avatar
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1 answer
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Vectorizing Dynamic Time Warping (DTW) algorithm

Dynamic Time Warping algorithm has a terrible convergence O(M x N), where M and N are the lengths of the sequences being aligned. This is due to the cost matrix computation, where a sequential ...
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How can I remove contamination from a 1D signal using a U-Net?

I'm working on a project regarding removing contamination from a 1D signal and I'm running into odd problems. The Dataset I construct the training set myself by taking 1D "pure" signals from ...
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Kernel Filter size and sampling frequency

I was wondering if I could understand the relationship between kernel size and sampling frequency. I was reading this paper and on Pg 6-7 ("In block-1" section), I read that kernel size of ...
Sarvagya Gupta's user avatar
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450 views

Help understanding lilliefors test

I need some help understanding the meaning of the lilliefors test. As far as I know, the lilliefors method provides a measure of normality of a data set. That is, a measure of how the images (values) ...
GGChe's user avatar
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Is it possible to define the wavelet phase difference for only one signal?

The wavelet phase difference between two signals $x(t)$ and $y(t)$ is derived using the real and the imaginary part of cross wavelet transform $W_{x,y}$. (Let us consider e.g. the Morlet wavelet as ...
Mark's user avatar
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1 answer
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What is Hamming Window in Audio Analysis?

I am reading this paper. The paper writes ... an input waveform of t seconds is converted into a sequence of 128-D log mel filter bank features computed with 25 ms Hamming window every 10 s. The ...
onexpeters's user avatar
1 vote
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636 views

Detect periodes of irregular patterns in time series data

This plot shows hourly time series data of a households power usage. The house is only occupied for short periods. What simple alg. or technique can I use to find the start of these irregularities? ...
NorwegianClassic's user avatar
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Uncovering which frequencies two systems are communicating on by observing reoccurring time correlated signals and their frequencies

Suppose you have a set of communication systems {A, B, C, D}, which speak with each other as well as other systems not contained in the set. Our concern is how they speak with each other. They ...
Michael's user avatar
2 votes
1 answer
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How can the autocorrelation function of an oscillating time series always be positive?

I have an oscillating time series which has a Lorentzian shaped power spectrum, centered about a dominant frequency. After taking the autocorrelation of this time series, I see that it's nearly always ...
Thermodynamix's user avatar
3 votes
1 answer
304 views

For time-series forecasting , is it correct to use signal decomposition methods (e.g., EMD or ITD ) to pre-process the dataset?

Specific description of the signal decomposition issue in time series forecasting While we forecast the time series with various deep learning models, signal decomposition like EMD (Empirical Mode ...
Jasonmils's user avatar
1 vote
0 answers
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Guessing filters from responses to step signals

Consider a signal $X$ filtered by a kernel $p$ with finite support $[t_0,t_1]$ and $\int_{t_0}^{t_1}p(t)\,\text{d}t = 1$, yielding the response function $$\overline{X}(T) = \int_{t_0}^{t_1} X(T + t)\ ...
Hans-Peter Stricker's user avatar
2 votes
1 answer
150 views

White noise does not contradicts Wide Sense Stationarity?

White noise is usually defined as a wide sense stationary (WSS) process $N=\{N_t|t\in T\}$ (for $T$ a time index set), that has a constant power spectral density, say $S_{NN}(f)=\sigma^2$. Since the ...
C David Reinach's user avatar
3 votes
1 answer
146 views

Two audio signals in phase at lag = 0,1 but positively and negatively?

Imagine you have two time series of audio signals. You run a time lagged cross correlation analysis and find there is a significant correlation between them at lag = 0 and lag = -1. The correlation at ...
user10136297's user avatar
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How Can I Reduce Similarity Analysis of Multiple Time-Series Vectors into a Single Value?

I have ~15 independently-sourced vectors with about 1600 samples in each. They are basically continuous, ~1 Hz, from t=0 to 22 minutes. The nature of the dataset is such that the signals are ...
Clayton Schneider's user avatar
1 vote
0 answers
30 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 ...
fsp-b's user avatar
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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 ...
Fuhan YANG's user avatar
1 vote
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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 ...
Ninjadog's user avatar
1 vote
0 answers
217 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 ...
Tino D's user avatar
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1 answer
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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 ...
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