Questions tagged [signal-processing]
For questions about processing signals of any physical quantity, with the help of Machine Learning or Artificially Intelligent algorithms.
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How does a zero-order hold kernel in a Convolutional Neural Network look like?
Several papers co-authored by Hitoshi Kiya propose to use a fixed convolutional layer with a zero-order hold kernel to avoid checkerboard artifacts in CNNs. [1, 2, 3]
While there is plenty of ...
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Is my 1D signal using CNN & RNN regression reasonable?
I want to know if my impact-echo signals are proper with CNN or RNN regression model.
I got some simulated signal, as following shows.
In previous research, people mostly consider frequency or even ...
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How can I transform a signal into another using supervised learning?
I'm trying to transform a signal into another using supervised learning. My main goal is to create a model capable to transform a raw signal (Blue Line) into something similar to the "ideal" ...
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AI in signal processing: how to reduce data volume
I am studying 5G technology, where AI/ML is integrated to imporve performance. I am not expert in AI/ML and I am sorry if my question is stupid.
Honestly i am quite not sure If I understand the main ...
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Which neural network/ML framework to use for a partial amount of a fixed-time amount of sensor data?
Assume I've run a set of initial experiments. For each experiment, I have a set of input signals $I_e(i, t)$, and output signals $O_e(j, t)$ for $i \approx 10$, $j \approx 10$, and $0\leq t\leq t_{max}...
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What is the training accuracy of this model?
I’m trying to classifiy ECG signals using LSTM and MATLAB, the above plot shows that the training accuracy of the system is 100% but when I apply this code to calculate and get the accuracy I get only ...
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How can I learn to transform one input signal (time series) into another?
I'm posting this question here because I've been trying in vain to solve a problem for weeks and I hope some of you might have some useful suggestions.
Basically, the problem is as follows. I have 7 ...
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Given the high resolution signal and the low pass filter (kaiser filter), is there a way to reconstruct the low resolution signal?
When we upsampling a discrete 1d signal by 2x, we first interleave the signal by 0, then pass through a low pass filter.
low resolution signal [x1, x2, x3, x4] -> interleave 0 -> [x1, 0, x2, 0, ...
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How does using complex weights in a neural network affect performance?
If you switch a neural network from real weights to complex weights, you're roughly doubling the size of the network, and increasing the computational load by a factor of 2 to 4. My question is, in ...
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Is it effective to use deep learning method to produce a 1D signal as output from a 2D image as input?
I have a 1D signal that will produce a 2D image after some image processing algorithm. Would it be possible and effective to use deep learning method to reproduce the 1D signal if I have the 2D image ...
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How to detect the sine wave signal with different frequency using neural networks?
I'm wondering if there is a way to use a neural network that can detect the noisy sine wave, where the frequency is not constant. In other words, I'm not looking for a solution that would detect the ...
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Can I call any function a signal?
While reading the Notation of the paper titled Geometric Deep Learning Grids, Groups, Graphs, Geodesics, and Gauges, I came across the following notations.
$$
\...
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Is it a good practice to pad signal before feature extraction?
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 ...
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What are some of the main high level approaches to applying ML on kinematic sensor data?
I've just started a project which will involve having to detect certain events in a stream of kinematic sensor data. By searching through the literature, I've found a lot of highly specific papers, ...
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Deep Learning based image restoration using multiple frames
Suppose we have a sequence of still images each of which has been contaminated by some particles(ex, dust/sand/smoke) making the images very poor in certain areas.
What architecture would be best to ...
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Which type of feature extractor do you suggest to classify sensor data?
I have IMU (Inertial Measurment Unit- 6 axis) sensor data. The sensor attached on a car and 7 different drivers wipe on same path. I want to extract features and classify drivers. Which type of ...
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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 ...
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Aren't all discrete convolutions (not just 2D) linear transforms?
The image above, a screenshot from this article, describes discrete 2D convolutions as linear transforms. The idea used, as far as I understand, is to represent the 2 dimensional $n$x$n$ input grid as ...
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What's the best method to predict/generate signal from one sensor (source) to signal from another another (target)?
I was wondering what is the best method out there to find relationship between two 1D signals so that I can predict/generate one (source) from the other (target). For example, let's say that in ...
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Determine Frequency from Noisy Signal With Neural Networks (With Adeline Model)
I'm trying to determine the frequency from a signal with NN. I'm using the Adeline model for my project and I'm taking a few samples in each 0.1-volt step in a true signal and a noisy one.
First ...
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Analyzing vibration using machine learning
I would like a few suggestions on an idea that I have -
I am trying to make a musical instrument (percussion), whilst just having a PVC disc. I am hitting the disc in a variety of styles (in order to ...
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How can I remove the noise from an EEG signal?
I am working on a project that takes signals from the brain, preprocesses them, and then makes the machine learn about what human is thinking about. I am struck on preprocessing the signal (incoming ...
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How can I combine the readings of multiple lidars into 1 point cloud? [closed]
I have a car with 8 lidars, each with a field of view of 60 degrees. My car looks like this:
How can I merge all the lidar readings into 1 point cloud?
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Can a neural network be used to detect sine waves?
I am recording the vibrations of an AC Motor (50Hz Europe) and I am trying to find out whether it is powered on or not. When I record these vibrations, I basically get the vibration values ($-1$ to $+...
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How can I detect fast and slow motion in videos?
I'm trying to detect if a given video shot is fast or slow motion. Basically, I need to calculate a "video motion" score in a given video sequence, meaning how fast or slow motion the video is. For ...
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Can AI be used to reverse engineer a black box?
A while back I posted on the Reverse Engineering site about an audio DSP system whose designer had passed away and whose manufacturer no longer had source code (but the question was deleted). ...
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What is a temporal feature?
What is a temporal feature, what features make something temporal in nature? Is this problem agnostic? How does it change from different fields of study?
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Is it possible to clean up an audio recording of a lecture using some type of AI system?
Is it possible to clean up an audio recording of a lecture from a smartphone (i.e. remove the background noise) using some type of AI system?
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Can I reduce the "number of weights" in CNN to 1/3 by restricting the input as greyscale image?
In a CNN, does each new filter have different weights for each input channel, or are the same weights of each filter used across input channels?
This question helps me a lot.
Let, I have RGB input ...
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Is there a way to perform pattern recognition without a labeled training set?
I have a 10GB file of a time series 1D signal. I want to find some patterns within this signal, I know CNN's are great for this but the problem is I don't have any training data.
Now, I could, of ...
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Is there a "better" (signal-based) language for artificial intelligence [closed]
I assume, there must be "signal-driven" and maybe also real-time programming language, which based on connectivy-data more than variables (int, string, etc).
I would like to have a language without ...
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How much of a problem is white noise for the real-world usage of a DNN?
I read that deep neural networks can be relatively easily fooled (link) to give high confidence in recognition of synthetic/artificial images that are completely (or at least mostly) out of the ...
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Are there any studies which attempt to use AI to guess the human emotion based on the brainwaves?
Have there been any studies which attempted to use AI algorithms to detect human thoughts or emotions based on brain activity, such as using BCI/EEG devices?
By this, I mean simple guesses such as ...
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