Questions tagged [signal-processing]

Numerical analysis of a digitized signal

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How to decompose multiple periodicities present in the data without specifying the period?

I am trying to decompose the periodicities present in a signal into its individual components. Say the following is my signal: You can reproduce the signal using the following code: ...
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Finding a Size Invariant Pattern in Noisy Data

I want to find similar patterns in my data, I assume that the patterns will be of different sizes both in time and in amplitude. The usual distance metrics will not work here, since the window size is ...
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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? ...
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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 ...
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Applying ROC to evaluate time-series signal event picking model performance

I am trying to evaluate the performance of an event picking model that attempts to find the onset of a signal in a noisy time series. Data contains the true signal time (ground truth) and the ...
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What is the standard deviation of a signal composed of multiple signals

I have three time signals (u(i) ,v(i) ,w(i) ) for 360 positions, and thus the total displacement is calculated as: vel(i) sqrt(u(i) ^2+v(i) ^2+w(i) ^2), for each position the signals are an averaged(...
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Autocorrelation of sum of sinusoids

Consider a signal that is a sum of sinusoids, e.g. $x(t)=Asin(at)+Bcos(bt)$ Is there an easy and general way to get an analytical solution for the autocorrelation of $x(t)$? Is the best way to simply ...
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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 ...
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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 ...
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Model Architecture for Mapping Audio from Low-Quality Space to High-Quality

I am doing a side project, where I am planning on recording with a bad mic and a good mic concurrently, and am trying to make a model to map your low quality audio to the high quality space. First ...
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Understanding centralization and normalization

I have the following task where I am given an audio signal: Center the signal (subtract the mean value) and normalize to a dynamic range of -1 to 1 (divide be maximum of the absolute value). If I ...
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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)\ ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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?
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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,...
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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 ...
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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 ...
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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 ...
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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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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 ...
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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 ...
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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 ...
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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 ...
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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(...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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} \ \ \...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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. ...
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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) ...
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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-...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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