Questions tagged [feature-extraction]
The feature-extraction tag has no usage guidance.
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Which filter is suitable for reducing noise in feature detection?
I have a feature detection algorithm that is called FAST - Feature Accelerated Segmented Test.
It's a very fast algorithm for finding feature points, e.g "corners" inside an image. Here is ...
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MFCCs are useful for speech & music analysis. For music, why isn't the 12-tone scale used instead of the mel scale?
As far as I understand, the mel scale was developed empirically using something akin to "just noticeable differences" in a subject's perception of frequencies. The scale ends up being a log ...
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Isn't the MFCC feature good for real time applications?
I am interested in the audio classification problem. You can think of the problem as ML, but my question is from a signal processing perspective. I am questioning the classification performance of ...
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Feature for a signal classification
Consider that i have N signals of electricity energy consumption (from N different consumers) that ha 24 measurements over a ...
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How to extract the oscillation frequency from the following signals?
Signals contain both transient and stabilizing processes.
In the transient process, the signal exhibits oscillatory behavior with damping. Is there some efficient algorithms to extract the frequency? ...
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Is lock-in amplifier a correct approach?
I have a noisy signal which is the voltage output from a photodetector circuit. I have researched about extracting signals, and found that lock-in amplifiers are one of the main approaches for the ...
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Lock-in Amplifier: How to improve the output of lock in amplifier?
I tried to extract a pure signal from the noisy signal using a lock-in amplifier with the help of python code.
The output is from a photodetector circuit.
These are the reference signal, expected ...
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How to simplify several cross correlations used for feature extraction
I have a system where I cross correlate recorded audio signals with several smaller generated signals to extract feature vectors that later I use in a kNN matching. The system works the way I want it ...
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Can the FFT be employed to make a dataset as Multivariate Normal distributed?
I have dataset composed of a large number of images with a large size (i.e., 32x32px), and I'm trying to adapt a feature extraction framework which assumes that the input dataset is Multivariate ...
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Anonymize / Obfuscate speech when doing audio classification
Let me preface that I am new to audio processing and audio analysis ;) (I asked the same question on reddit, I wanted to increase it's reach))
I am trying to classify specific events (like a gong or ...
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STFT Audio processing with zeros
In this paper they preprocess an audio file with a STFT and then apply two equations to the computed matrix.
I tried implementing all of it in python and used librosa for the STFT.
The issue I ...
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FFT of frequency bands and limited amplitude modulation
In this paper they preprocess a cent spectrum of an audio file.
For comparison of audio files they create the Logarithmic Fluctuation Pattern(LFP)(p.3).
For this I have to use a FFT on each frequency ...
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Resource recommendations to learn audio processing
I'm looking for some good resources to learn audio processing for a machine learning task based on classification of users as either 'COVID-19 positive' or 'COVID-19 negative' based on their cough ...
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How to Implement the Sub Pixel Refinement Phase in Scale Invariant Feature Transform (SIFT)
I'm trying implement SIFT(Scale-invariant feature transform) myself. I've read some documents and stuck at Sub-Pixel Refinement - left without knowing how to actually implement it in code. I do ...
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Short time energy or root mean square?
What is the difference between Short time energy(STE) or root mean square(RMS), Librosa library offers a function to calculate RMS but doesn't offer one to calculate STE, which gives the impression ...
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The SIFT Descriptor and Image Resolution
When reading about SIFT I read halving resolution is the same as increasing $\sigma$ of the gaussian in terms of feature detection but reducing resolution has the advantage of reducing processing.
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preparing the ct scan data of a patient, using a visible feature in the mri image
A dataset of mri and ct scan images of patients has been prepared. There is a feature /damaged area/ in the mri image that is easily visible. But the injury of this area is not visible in the CT scan ...
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Extract diagonal area from image
I have a gray scale image of fibres in different orientations. My goal is to mark the area where the fibres have a specific angle and neglect the rest. In the future it should be an automated process ...
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Why is scaling of images / pixels into `[0, 1]` range performed before SIFT (Scale Invariant Feature Transform) algorithm?
The SIFT paper and the paper of Anatomy of the SIFT Method do not mention that the input images should be preprocessed (normalized, re-scaled) before feeding images into the standard SIFT algorithm. ...
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Log Mel Spectrogram vs Log Mel Power Spectrogram
I'm doing some feature extraction on audio signals.
$M$ being a mel filterbank matrix, and $S$ being the spectrogram (extracted from the Short Time Fourier Transform of my audio signal), we can ...
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Power Spectrum for feature extraction
I want to extract frequency domain features for my machine learning model, and to do so I have calculated the power spectrum of the EMG signal. One of my first questions is that my frequency is just ...
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Adjust Wavelet output to CNN
I want to extract features from the signal using DWT and then feed the results into CNN, after calling the 'dwt' library function I got a runtime error.
...
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How to get epochs from eeg data in python?
I am working on sleep stage classification and have EEG data from the link : https://physionet.org/content/hmc-sleep-staging/1.1/
I am using pyedflib to extract the data.
I am tring to extract the ...
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Should i normalize FFT signal with z-score?
I am working with EEG data (time domain) in a machine learning task, where each input signal must be mapped to a class/frequency. I am using FFT in order to get data in frequency domain and make ...
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SURF normalisation and Haar wavelet
I am reading the SURF paper (Speeded-Up Robust Features (SURF)) but can't understand two things. In 3.2 it says:
Furthermore, the filter responses are normalised with respect to their size. This ...
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Extracting "frequency domain features" from signal for classification
I have a real signal with only nonnegative values (actigraph measurements). Many papers say they use "frequency domain features" for classification, such as mean, variance etc. But I'm ...
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287
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Accurate phase calculation in sinusoidal linear regression?
I've been trying to work out a way to minimize the error in phase calculation. The underlying model is the following
$$s(t) = \sum_{i=1}^{M} A_i\sin\left(\frac{2\pi t}{T_i} + \phi_i\right) + \...
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Understanding GCC-PHAT as a Feature
I'm working on a sound source localization project and am interested in using GCC-PHAT as a feature. I'm quite new to this transformation and I've been reading a few papers about it and I'm still ...
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What are features in signal processing using a more tangible or less abstract description
I'm learning about signal processing for music, and in my reading I keep finding the term "features". I find that they never really give a concrete example of what they mean by "...
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Logarithmic Amplitude Spectral Subtraction
I'm diving into audio processing and I'm trying to wrap my head around spectral subtraction. I learned there are different approaches do it based on power, magnitude, oversubtraction. My task is to ...
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Feature extraction for IRIS recognition
What are types for feature extraction in iris recognition? How can I divide it on types which detect phase, edge, etc.?
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feature extraction techniques for iris recognition
I want to ask how I can divide feature extraction techniques to feature detectors and feature descriptors. I have big problem how to understand it. For example I can use Gabor filters (feature ...
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DWT and FFT on a very low frequency signals
I am trying to extract features from a very low frequency signal of $0.1$ HZ and less than $100$ samples. Is it advisable to use FFT and DWT on this signal?
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Invariances of FFT-based Image-Registration vs. SIFT-Features
TL;DR: I don't understand how invariant FFT-based image-registration techniques are to object alterations (scratches, marks etc.) in comparison to SIFT-features.
I want to build kind of a feature-...
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ORB Implementation, Scale Pyramid, rFAST
Reading this ("ORB: An efficient alternative to SIFT or SURF") paper, I am not certain how they use the scale pyramid.
It says it is created and FAST feature detectors are employed at every ...
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How can I better extract the hidden images in FEZ's soundtrack?
I believe I have found a (new?) secret in FEZ's soundtrack, but my visualizer isn't clear enough to capture it 100%.
In FEZ's soundtrack (by Disasterpeace), there are "secrets"- stuff like ...
Skye Mors
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Working with a sound's magnitude instead of amplitude
I'm working on a project, where we're recording sound with a piezo-disc which looks a little something like this:
Now, unless we're doing something horribly horribly wrong, I've discovered that we're ...
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What is the purpose of dividing an audio signal into segments and analysis each segment?
I read bunch of materials for extracting feature from audio signal and they all tell me to break signal into segments, why don't we analyze all the audio signal? I don't know what are the advantages ...
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How can I stitch bottom of some image to topside of another image by erasing as much overlapped image as possible?
I have 2 images of size 1920*1080 and I want to try image-stitching on them. These are images (images are from wind turbine blade.)
This is the first image
And the second image.
I already have ...
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Why do we need multiple layers in each octave and multiple octaves in SIFT?
I skimmed through the SIFT paper. I understand that there are multiple octaves, which are composed of multiple layers. The layer $k$ of an octave (btw, where does this name come from?) corresponds to ...
user40095
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KLT for an ECG Signal
I am currently searching for methods of feature extraction from an ECG signal and I've stumbled upon the Karhunen–Loeve Transform. I've read some papers and I think I get the basics but my question ...
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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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315
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Understanding the Threshold Process in Harris Corner and Edge Detector
I have read the paper Chris Harris , Mike Stephens - A Combined Corner and Edge Detector about the Harris Corner and Edge Detector, but I didn't understand the point that after low and high threshold ...
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log base in log-power spectral features
Is the log in log-power spectral features natural logarithm, or common log of base 10? does it even matter?
I found sources for how to calculate it, but they all call it 'log'.
no one mentions the ...
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What is the HOMING operator?
I was reading "Optical Recognition Systems" book and in chapter 3.3.2.3 where methods for full genetic programming are described, a paper is describing an ADF (automatically defined functions) like ...
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which features fundamental frequency is correlated?
I'm unable to compute $f_0$ (fundamental frequency) in librosa feature extraction. By ready much in github issues check the second comment. I see that $f_0$ is ...
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how to extract a radio signal features with python?
I have a dataset of radio signals that i want to classify. So, I decided to extract features. After some googling, I have noticed that I have to calculate moments, cumulants, Kutosis and skewness to ...
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Calculate the tension of a triad of notes
I'm trying to implement and calculate the tonal tension of a triad (and its harmonics) following the definition given here: https://pdfs.semanticscholar.org/f05e/56c9548fa18c64efeed248742e3a6afb0c02....
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What features can I extract from optical fibre signal?
I have sensors placed in pipelines and I have amplitudes coming from it. I want to detect the type of activity taking place at the sensors(like digging,tunneling,clamping etc). So I have a signal ...
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Fast Vocal Tract Length Normalization(VTLN) implementation?
My team is building a speech recognition model. As far as I know, Vocal Tract Length Normalization (VTLN) is an effective warping method that may help improve the model performance by diminishing ...
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