# 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: ...
41 views

### 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 ...
• 115
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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? ...
17 views

### 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 ...
8 views

### 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 ...
10 views

### 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(...
13 views

### 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 ...
1 vote
78 views

### 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 ...
25 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 ...
5 views

### 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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52 views

### 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 ...
1 vote
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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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1 vote
36 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 ...
22 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 ...
1 vote
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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. ...
• 123
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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) ...
• 625
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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 ...
23 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 ...
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1 vote
65 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 ...
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1 vote
61 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 ...
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1 vote
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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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