Questions tagged [machine-learning]

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10 views

evaluate and process videos with over exposure problems

With a video processing task, how to evaluate and process the video data set that has over-exposure problems?
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8 views

MWE of zero-sum mask in the context of machine learning

Federated Learning seems to have a lot of potential, I understand it can be done using different techniques, e.g. functional encryption, etc. The simplest seems to be zero-sum masks. For TensorFlow ...
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24 views

Why do people use STFT as a preprocessing step to using CNN?

Just briefly looking up some research papers on audio data and I have come across some papers that use STFT as a preprocessing step to using CNN. Why is this the case? What are the advantages and ...
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1answer
28 views

Automatic detection of noise-only segments in audio

This is my first question for this community. My connection to DSP comes via machine learning/deep learning, and I am working in the Python ecosystem. As a preprocessing step to audio classification, ...
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19 views

How can a CNN account for spectro-temporal constraints in neural data?

What are there the best ways to leverage the unique "geometrical" constraints of spectro-temporal signal representations (architecture, filter shapes, data augmentation, etc.)? For example, ...
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20 views

Hamilton-Jacobi-Bellman equation vs Riccati equation

Most of the literature on Reinforcement Learning discuss Hamilton Jacobi Bellman equations for optimality. In dynamics however Ricatti equations are used. I am curious if there are any parallels ...
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26 views

How to deal with different audio formats for audio classification?

I am working on an audio classification problem statement to classify between two audio classes. I have collected samples from jotform, they are providing audio widget to collect .wav audio but it ...
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1answer
30 views

Cochleagram vs STFT for CNN-based speech segregation

I’m working on a project (python-based) that would use ideal ratio masks (IRMs) as a basis for cleaning noisy speech in various environments. Specifically, this will be accomplished through the use of ...
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1answer
21 views

FFT question as it relates to Neural Networks / Supervised Learning Models

So I've been researching wavelet transforms and FFT. I want to feed wavelet transforms of a 1D signal in time into a neural net and train against a target variable at each time step. The idea being ...
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2answers
35 views

Understanding MFCCs

I am doing research about emotion recognition from speech, by applying machine learning. Most papers are recommending using MFCC features. Therefore, I am currently trying to understand the underlying ...
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31 views

How to save MFCC features and frame energy of several wav files to one .npz file for machine learning training?

I am creating a English-Italian corpus of around 100 hours of English audio from LibriVox aligned with their Italian textual translation. The alignment is at a sentence level and the corpus consist of ...
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27 views

Classifying motor imagery from EEG data: Feature selection

I am an undergraduate student who is trying to classify motor imagery from EEG data! I have no experience working with EEG or any neuroscience background, I only have a very basic knowledge of how ...
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36 views

Wind turbine vibration analysis approach

This is going to be a bit of a long post. My questions are: Do the 5 signal processing steps look "correct"? Next steps could be to perform order analysis, and extract features from time, frequency, ...
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31 views

How to approach Feature Extraction and Feature Selection part in machone learning in python?

I am a bit new to machine learning and I have the following questions: Question 1: When dealing with feature extraction with signals from sensors, what is the typical approach to extract features ...
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53 views

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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73 views

How do i calculate the accuracy of an algoruthm?

I'm doing research on an machine learning image processing algorithm (segmentation algorihtm)i.e. computer vision performs when an image takes as an input and objects in an image are segmented and ...
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9 views

Using MFCCs for acoustic machine failure prediction

MFCCs are ubiquitously extracted for speech processing tasks, but I would like to know how suitable they are for non-speech processing tasks. Intuitively, it is my understanding that MFCCs are ...
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33 views

Is it useful to apply edge detection filter as a preprocessing step before applying CNN?

Background: my future boss would like me to apply a number of low level filters to industrial images before applying machine learning methods on them. From my knowledge this doesn't make any sense as ...
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16 views

How to update Kalman Filter with n-th order state translation?

In Kalman Filter, the hidden state translation is defined by $X_t=F_tX_{t-1}+W_t$, where $X_t$ can be a vector or a single value. This is actually derived from Bayes filter, in which 1-th order markov ...
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16 views

How to reliably identify the starting point of multiple instances of a noisy signal?

I have collected data from a person performing the same action for 10 iterations. As shown in the figure, signals from all iterations look very similar, but contain some unwanted noise in some ...
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11 views

Is sparse dictionary learning just a subset of non-negative matrix factorization?

I saw some people argued that their method should be called sparse dictionary learning or NMF. What are the differences between these two terminologies? If the dictionary is given or fixed, the ...
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1answer
36 views

What's the purpose of augmenting an image with a random “gradient”?

I have the following Python code ...
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0answers
43 views

Frequency contour detection in spectrogram

I have an image of a spectrogram and I wish to detect the tracks/contours of prominent frequencies present in the spectrogram. In the end, I want to be able to get various prominent curves from the ...
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1answer
35 views

How to prepare different input size in CNN

I'm doing a research about personality identification based on their signature using CNN method, however the learning feature for the personality traits have a different input size. I understand that ...
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1answer
56 views

Modeling audio effects, reverse engineering

Okay so I'm relatively new to signal processing and don't know the specific terminology to help narrow my search, but I'm looking for advice in the form of books, papers, references, etc that can help ...
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1answer
69 views

machine learning on multiple signal at once

I am very new signal processing and I am asking this with the expectation that someone gives me a concrete guideline on how to approach my problem. The problem I need to solve is that I have 2 ...
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1answer
47 views

Deep Learning: Classification vs. Convolution for Signal Restoration

Assume we have vector $X = [x_1, x_2, x_3, x_4 ,..... x_N], ∈ -1,1$. Therefore the value of $x$ is either 1 or -1. The vector $X$ is convoluted with random generated vector $Y$ whose length is the ...
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1answer
29 views

How to classify audio files based on their ZCR, energy, and enthropy of energy features?

Let's say I wanna classify the audio files of race car exhaust and normal car exhaust. I attain few features such as ZCR, energy and enthropy of energy from the training set and I wanna build the ...
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35 views

ADSR parameter identification from envelope data

I'm trying to work out how to identify (or predict) ADSR parameters (attack time, decay time, sustain level and release time) from envelope data of real signal (or 'raw' data, but I think getting it ...
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38 views

Deep Learning based NDA Channel Estimation

I was wondering if it is possible to use Deep Learning to estimate the Channel Impulse Response for NDA synchronization? I understand Deep Learning is not normally used for regression problems but I ...
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1answer
55 views

STFT to spectrogram

I would like to know whether I am correct in my understanding of going from STFT to a spectrogram. My goal is to convert a spectrogram back to a wav file. If I have my STFT: ...
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0answers
27 views

CWT coefficients as features for ML algorithms

I use CWT coefficients as features in ML algorithms and then I did the feature selection using the chi-square test but recently I figured out that the chi-square test can only be applied for ...
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0answers
14 views

How can I cluster signales by their different noise patterns?

Given be a set of signals (e.g. images) coming from one or more sources (e.g. different types of cameras) is it possible to find out, which signals come from which source? I consider this question ...
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33 views

Using unlabeled EEG data for Machine Learning

I am working on a project that is basically a game for motor-paralyzed people. It should take an EEG signal from FP1 channel of brain and then after processing it should generate command for the game ...
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51 views

How to normalize features after windowing for future test data?

I have an eeg data about 10 minutes. I want to extract some features (e.g. statistical features, power spectrum features, interchannel features, . . .) from this data to apply them to the machine ...
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39 views

Detect Trigger in vibrational data

I'm trying to classify states with a 1D-CNN-Network structure. Therefore I need to analyze the incoming vibrational data in a certain window. Two examples of such signals are shown below. I'm ...
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1answer
62 views

Feature Extraction of Insect Sounds

First of all, forgive me if I sound stupid. I am still learning Audio Processing. So I am trying to make a machine learning model for detecting a specific insect through sound. Here is a sample raw ...
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1answer
47 views

Compensate for microphone?

I have trained a machine learning model to recognize a specific audio event from mel spectrograms, and it works pretty well on a hold-out test dataset. I now want to run the model on a device and feed ...
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0answers
19 views

Is it possible to restore audio that has been tonally modified by a phase vocoder?

My objective is to apply some process to the modified audio that allows me to obtain the original audio. I think it can be solved through machine learning techniques. I don't know if there is any ...
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35 views

convolutional deep Neural Networks for matrix

I have a basic question about using convolutional neural networks. It's not my field but I'd like to read and understand about it. What I know that convolutional neural networks is used for image ...
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0answers
143 views

Removing silent frames from audio files using MFCC+Z-Score

So I'm using ESC-50 audio database, and the files have some silent frames. While trying to reduce the weight of these silent portions for machine learning later, I noticed something. The silent frames ...
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2answers
103 views

Transforming RGB images including NIR to LAB

I'm relatively new to image processing, so I hope I don't ask trivial questions. I have some images that I want to use in a machine learning context. The images have four color channels: RGB and NIR (...
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1answer
59 views

Which Machine or deep learning algorithm is appropriate of this issue?

Suppose I have $n$ features as $Y$ = ($y_1 , y_2 ...., y_n$), and a matrix of $J$ of dimension $M$x$N$, one feature of $Y$ is selected randomly to be convolved with one random column of $J$ resulting ...
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1answer
113 views

Machine learning for denoising MRI images

I'm currently a sophomore in college and pretty new to the field of research. I'm currently working on an existing algorithm for MRI de-noising and the results are nothing that great. I can't see how ...
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4answers
2k views

Apply Principal Component Analysis (PCA) for RGB Images

I've implemented a method to compute PCA on grayscale images. I haven't seen PCA on RGB images yet, which left me wondering if it is possible to perform it. With RGB images, is PCA done for each ...
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1answer
87 views

Can CV or DS be seriously considered DSP roles? [closed]

This is not a technical question but can have a yes/no answer, and a why statement. My understanding about digital signal processing (DSP) is that it allows to take real-world analog signals (1D, 2D, ...
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1answer
57 views

Can Convolution neutral network train(learn) separately (train different times)?

I am new in Convolution neutral network(CNN). My question is, is there a way to let CNN train separately? For example, at very beginning, CNN only need to learning hardwriting 0 and 1. After the ...
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1answer
27 views

Is it possible to use k-means algorithm with just one vector

Suppose I have a vector $X = (x_1, x_2 , . . . ,x_n)$, $x_i$ is the maximum of $X$ and $x_k$ is the minimum. Is it possible to use k-means algorithm to cluster the values in vector $X$ into two ...
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0answers
53 views

Continuous-time RNN and Shannon sampling theorem

The most-used discrete-time RNN equations used in Deep Learning these days are those of Elman: I have seen two very different continuous-time version of these, with different justifications. The most ...
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26 views

How to classify overlapped signals?

A known signal, signal 1 got overlapped with an unknown signal. Likewise, signal 2 overlapped with another unknown signal. The problem I face now is how to classify the overlapped signals based on the ...