Questions tagged [deep-learning]
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52
questions
1
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1answer
28 views
The Meaning of $ \mathbb{E} $ Operator in the Pix2Pix Loss Formula of a Neural Network / Convolutional Neural Network
I've been observing the Pix2Pix Paper - Image to Image Translation with Conditional Adversarial Networks and wondered on formulas. For example, the objective of the CGAN is:
,
where x - observed image,...
0
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1answer
26 views
References of deep learning and ai for dsp researchers [closed]
Are there excellent references for machine learning, deep learning and ai for DSP researchers?
0
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0answers
42 views
pre-trained deep residual networks based on transfer learning
According to the following paper:
Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun; Proceedings of the IEEE Conference on Computer Vision and Pattern ...
1
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0answers
19 views
Backpropagation in a Network [closed]
Hi does anyone know how to solve this. Im really struggling to figuring this one out.
I am unclear of how the backpropagation part would work in this network and how to calculate it for each weight. ...
0
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0answers
9 views
FPR for time series of 1 hour epilepsy recording
I am working on epilepsy seizure prediction classification using a convolution neural network. My dataset consisting of multiple recording of each record for 1 hour, first I segmented each record into ...
0
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0answers
8 views
Upsampling class activation maps for discriminative feature localisation
I am currently reading a paper by on learning deep features for discriminative localization where the authors propose to use class activation maps to learn discriminative localised features. The ...
0
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0answers
14 views
Use of spectrograms in using deep learning on EEG related problems
I am currently working on a classification problem related to EEG signals. My professor asked me to convert those signals into their spectrograms and try various convolutional neural networks on them. ...
0
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0answers
33 views
Deep Learning for Online EEG Denoising
I'm currently working on the development of deep learning methods for online artifact removal from EEG signals. I'm interested in removing common biological artifacts such as EOG and EMG individually ...
0
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0answers
30 views
Audio RMS normalization prior to a DNN classification
I want to normalize my data prior to a deep neural network model (DNN). It was recommended to me to use real mean square (RMS) normalization for audio, though I am not sure this is the best for a DNN. ...
1
vote
1answer
55 views
how to handle different durations of audio data?
I am new to signal preprocessing, I read about mel_spectrograms, MFCC's. Now I want to apply it and use the CNN model, But the data which I have for practice is having audio of different durations, ...
0
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0answers
25 views
multi-frame image restoration
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 approach would be best to teach ...
3
votes
2answers
181 views
Classic Signal Processing vs Deep Learning / Machine Learning (DNN / ML) Based Signal Processing
Are classic signal processing/statistics based approaches to optimum detection/estimation still relevant/important compared to ML based approaches using DL?
There was a time when speech processing was ...
0
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0answers
20 views
Pretrained networks with Adam optimizer?
I have tried different pretrained networks with Adam optimizer, and obtained high performance. However, when I tried it with Alex network, the performance was poor. Could you please help me ...
1
vote
1answer
25 views
Why not use synthetic datasets for training machine vision (deep learning) algorithms?
I am studying some examples of how to perform 3D instance segmentation in indoor scenes, and I have noticed many of the available datasets are from real environments. I was wondering with the ...
1
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0answers
28 views
what should be a good bibliography for deep learning in 2020
What should be a good bibliography for having a good overview of deep learning (on image/signal processing) in 2020 ?
0
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0answers
41 views
Remove noise from VGA image
I have 2 color images generated from same X-ray machines, but one of the image is obtained using VGA cable. Therefore the RGB intensities differ in the 2 images. I am using Object detection to detect ...
0
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0answers
56 views
PCA with CNN Tensorflow
I need to improve my model of Convolutional Neural Network (CNN). The goal is to recognize facial expression.
I've been using some strategies like dropout for regularization and Adam optimazer, but i ...
0
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1answer
49 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 ...
0
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1answer
59 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 ...
0
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0answers
29 views
Audio in Image,Graph or WaveForm Representation
Our task is to feed the audio data into the deep learning tensor flow model in the form of image representation(or graphical,waveform). Question is what is the best way of representing audio in image ...
0
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0answers
418 views
Understanding liftering as the final step in MFCCs features extraction
In the book here, they apply liftering, as a final step of MFCCs features extraction, to isolate the system component by multiplying the whole cepstrum by a rectangular window centred on lower ...
1
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0answers
32 views
Adversarial training: deep learning book
In the Deep Learning book of Ian Goodfellow, p. 261 it is shown how to build an "adversarial example" by adding to an image $x$ another image $x_{adversarial}$ build as epsilon times and image (same ...
0
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0answers
40 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 ...
1
vote
2answers
73 views
Could modulation/coding schemes be generated from an AI?
Warning: I'm a bit of a noob with respect to this entire field, but I took some classes on it (a few years ago) and found it fascinating.
Anyhow, as far as I understand it, modulation is the process ...
0
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1answer
32 views
Removing vocal but retaining all background ambient noises
I have one audio file containing human speeches, a lot of ambient noises like audience laughter, birds chirping, sounds of natures etc..
Now I want to separate it into two audio files where one ...
1
vote
1answer
68 views
Neural networks in system identification - What type of activation functions?
I made a free software for all operative systems, even Android. It's called Deeplearning2C. It can train a neural network and generate C code and MATLAB-code. C-code for embedded systems and MATLAB-...
0
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0answers
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 ...
0
votes
1answer
64 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 ...
1
vote
1answer
47 views
Are delta and double delta features needed when classifying with LSTM
I understand that the combination of MFCCs, deltas and double deltas is a good feature to be used with HMMs for keyword detection problems. HMMs are limited by Markov Property and this limitation is ...
1
vote
2answers
203 views
Augmentation for EEG signal classification using Deep Learning
Augmentation is a technique that we use in deep learning for expanding the training dataset. It includes different ways of modifying an image and adding it to the training dataset. My question is if ...
1
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1answer
343 views
How to express STFT and ISTFT as a 1d convolution and 1d deconvolution in tensorflow/keras
I'm trying to implement this paper in tensorflow and keras. At the end of section 3 it says.
...
2
votes
2answers
983 views
audio spectrogram normalization
I am performing language classification from audio signals using mel-spectrograms as my inputs for a ResNet. It works well as long as all of my audio data from different languages is from the same ...
2
votes
1answer
234 views
How to Remove the Patch Artifacts of Neural Network Denoising Process?
I have written a python script which uses the Noise2Noise: Learning Image Restoration without Clean Data implementation of the Auto Encoder which is useful to remove noise from images. In the original ...
0
votes
1answer
52 views
What does it mean for a Wavelet transform to commute with translations?
Referencing this article here https://arxiv.org/pdf/1203.1513.pdf
It states "A wavelet transform commutes with translations, and is therefore not translation invariant". Now I understand why it is a ...
1
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2answers
582 views
What fps can be considered as a hardline for real-time performance? Is there any academic paper that describes this?
I am trying to design a deep learning based inferencing solution for security applications. My deployed program achieves an FPS of 15fps for classification. Can it be considered real-time?
6
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3answers
3k views
Neural Networks and Complex Valued Inputs
[not sure if this or stats.stackexchange was the correct location for this post, so put it on both for now.]
I've seen some recent papers describing complex valued neural networks like this one: Deep ...
2
votes
1answer
148 views
Is deep learning the only way to detect humans in a picture?
I'm looking for a way to detect humans in a picture. For instance, regarding the picture below, I'd like to coarsely determine how many people are in the scene. I must be able to detect both standing ...
1
vote
1answer
79 views
In Convolutional Neural Nets, what do convolutions look like?
The early stages of the Convolutional Neural Networks are performing classical convolutions with a certain kernel size on the input image. Is is possible to express in common terms the type of ...
0
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1answer
53 views
Does the audio signal in the time domain change much after a RMS from one person to another?
I would like to train a deep neural network to perform lip syncing given an audio input like in this article.
I want to train my neural network only on Obama speeches like done in the article and ...
2
votes
1answer
304 views
RNN as posterior probability estimation in speech recognition with HTK
I'm new to speech recognition and deep learning and in a learning phase.
I'm trying to follow this paper to learn how to use RNN as posterior probability estimation in an HTK environment. The paper ...
2
votes
1answer
1k views
Training a CNN-HMM model
I am currently trying to train a CNN-HMM acoustic model for speech recognition.
The CNN model is able to detect a center monophone given a context window of x (limits has not been tested yet - but ...
0
votes
1answer
48 views
Deep learning model for phone recognition - issues with dimension the model
I seem to have some problems understandind how the model described in this paper has been designed
This is what is written about the model dimension..
...In these experiments we used one ...
1
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0answers
75 views
Pose-invariant face detection and recognition
I would like to detect faces and later apply recognition algorithms to surveillance videos. Ideally, the application should run in real-time (or near real-time) and transfer the data first to an ...
1
vote
0answers
2k views
Image preprocessing for facial detection->embedding->clustering pipeline
I am trying to implement an end to end pipeline for facial clustering so that it can group people with the same faces. This will be quote a long post as I know that this is a very broad topic, so I ...
1
vote
1answer
141 views
image reconstruction using a set of kernels
I try to be brief. For what I understood Convolutional Neural Network CNN for style-transfer extract/learn the main features of source Image. I think these features basically are Kernels (of different ...
-1
votes
1answer
473 views
Is image annotation a hard AI problem? [closed]
From Wikipedia "In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems is ...
3
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3answers
4k views
Principal Component Analysis (PCA) on Convolutional Neural Network (CNN) Features
Please, I have a question regarding PCA and features which are extracted from a convolutional layer. link
if we have a test dataset , and we extract all conv features of all images at test dataset ...
7
votes
1answer
10k views
When is a network called end-to-end training?
In machine learning, we often see the expression "end-to-end" learning (or training). However, I do not know that it means. When is a network called end-to-end training? How to recognize a network is ...
4
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2answers
8k views
Can deep neural networks achieve real-time video analysis?
Recently, convolutional neural network based, deep architectures (DNN) such as AlexNet and VGGnet have been very successful in image classification challenges (e.g. ImageNet) and action recognition/...
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11answers
20k views
Is deep learning killing image processing/computer vision?
I'm looking forward to enroll in an MSc in Signal and Image processing, or maybe Computer Vision (I have not decided yet), and this question emerged.
My concern is, since deep learning doesn't need ...
