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Questions tagged [neural-network]

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62 views

Recover Audio from Spectrogram Image

I have applied transformation(Constant Q Transformation) to my audio time domain signal, this gives me Frequency vs Time representation. Now, the CQT values consists of complex values(84*260), but ...
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20 views

What is the best input for de-noising autoencoder for sound data?

I am currently trying to build an autoencoder to de-noise audio data. However I have not found any good articles explaining about the input to the autoencoder, i.e. feature vector. As in speech ...
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1answer
70 views

Techniques to reject noisy neural network input

Suppose an artificial neural network is used to approximate a sine wave (shown in red in the graph below), given the linear input variable $x$ (scaled such that the ANN input is $x_{\rm nn}\in[-1;1]$)....
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1answer
105 views

Generalized translation on graph

David I.Shuman in "vertex-frequency analysis on graph" claims that,"we generalize one of the most important signal processing tools – windowed Fourier analysis – to the graph setting and When we apply ...
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1answer
67 views

“Bi Directional” Kalman Filter

I am working on a project in Object Tracking, i.e. need to predict the location of next bounding box. I used a Hungarian algorithm with a Kalman Filter which produced decent results. However, lots of ...
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1answer
346 views

Is there a penis detection demo similar to face detection?

Tutorial for face detection: Is there a script / tutorial / demo for penis detection? These guys ran into some issues: Fairly serious question, future of internet memes is at stake. Breast / ...
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1answer
979 views

Convolution and cross-correlation on 2D image (Example)

I have read that convolution and cross-correlation are the same thing, but convolution flips 180 degrees (images), or time reverses (sequences) the kernel, before performing the elementwise ...
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39 views

taking sample from probability distribution

How can we take sample from probability distribution obtained by softmax function output instead for example argmax ? what is the mean of taking sample from probability distribution??
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2answers
756 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 ...
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31 views

Image Classification with CNN

I'm going to use a Convolutional Neural Network to classify images into two classes as re-sampled or not. I need a large data set of resampled and non- resampled images this task. Could you please ...
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1answer
397 views

Downsampling audio for use in Machine Learning

I'm trying to use the work (Neural Networks) done in this repo: https://github.com/jtkim-kaist/VAD It says this: Note: To apply this toolkit to other speech data, the speech data should be ...
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16 views

MFFC - Source moving towards the recorder in time

I'm doing my master thesis, it's about classifying ships using sonars. I'm using MFCC to extract the features. The question that I have is, since the ship will move towards the sonar in time, will I ...
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1answer
68 views

Neural Network learning project based on 8 wave signals over 1 second at 1 sample every 10 ms ( hence 100Hz )

I'm currentely trying to train a neural network that can decide wether a pattern produced by the movement of a hand near capacitive sensors is as expected, or random. I have an MPR121 microchip linked ...
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1answer
123 views

Keyword spotting - constant phoneme length

I wish to implement a keyword spotting algorithm (in speech), on the basis of what was published in this article (Apple's Machine Learning Journal). The article describes a neural-network-based ...
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0answers
35 views

Proof Neural Network Robustness

I am working with simple autoencoders and would like to find a bound on the reconstruction performance $\vert \vert x_{\text{in}} - x_{\text{reconstr.}} \vert \vert $ or the robustness with respect to ...
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1answer
69 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 ...
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1answer
204 views

Pattern recognition in time series 4x3000 vector

I have a vector, here is a sample of some data from some heat flow data: I would like to identify features in this image. In the example above I have identified one feature I would like to ...
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0answers
24 views

Applying phone-effect on clean speech corpus without spectogram loss

I intend to train an end to end speech recognition engine for phone conversations. Most of the available corpus is crystal clear narrated data, so i'm looking for a way to bring my training data ...
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1answer
45 views

Generalizing the chain rule

Given the real-valued functions $f_1$ and $f_2$ with $x\in\mathbb{R}$, then $$ \frac{df_2(f_1(x))}{dx} = \frac{df_2(f_1(x))}{d f_1(x)}\frac{df_1(x)}{dx}$$ Is it then the case that if we also have a ...
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1answer
46 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 ...
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0answers
62 views

Neural Networks for rotated character (or shape) recognition

I have built a program that recognizes shapes and characters, using Neural Networks. Now, one of the main requirements of the task is that the program can recognize them regardless of rotation or ...
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2answers
370 views

Does an equivalent transformation of a signal to a spectrogram image exist in which the phase information is part of the resulting image?

I'm working on a research project where we would like to apply convolutional neural networks to an image representation of a signal. However, it seems that if I would use a spectrogram, I would end up ...
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2answers
234 views

Neural Network: Spectrogram Dimension

I like to work with a convolutional neural network in combination with a spectrogram as input. Assuming the spectrogram has the dimensions $T\times F$ (time$\times$frequency). Is it more natural to ...
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0answers
202 views

Proper way of representing Static, delta, delta delta in a plot

I am currently working on recreating the result of this paper. The paper is about applying cnn in speech recognition, in which cnn is used to for feature extraction, for which a proper way of ...
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1answer
320 views

What would the target matrix to train Neural Network?

I'm new at Artificial Neural Network and I'm using MATLAB developing Facial expression recognition and There are six expressions ; I'm not able to understand about How to create a target matrix? My ...
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3answers
1k views

Principal component analysis (PCA) on convolutional network 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 ...
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1answer
2k views

Spectrograms for neural nets

General Question Given an audio file, say a 16-bit wav, what are some standard methods to preprocess a spectrogram of this wav so that it may be fed into a neural net? Context In Lee et al's 2009 ...
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1answer
143 views

How to apply neural network parallelly on each part of image

I have an image which is divided into four equal size square blocks. I want to apply neural network for denoising. Usually, I apply on the whole image. But i was thinking that is it possible to divide ...
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1answer
52 views

Good Reference Problem to Test Filtering/Estimation Algorithms

I am looking to figure out if a current filter algorithm I have built could be useful for some problems I am looking into at work. It isn't a Kalman filter, but is instead making estimations using a ...
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1answer
81 views

Answering Machine vs. Human — Neural Network Features Selection

I've been tasked with creating an artificial neural network that can classify a telephone call as either answered by a human or an answering machine. My knowledge of audio processing is, mildly put, ...
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0answers
46 views

Feature maps for a Convolutional Neural Network

I hope this is the right place to ask this, so here goes: I am currently trying to implement a convolutional neural network in C++, but since I have no formal education in signal processing, image ...
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2answers
7k 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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0answers
47 views

Pearson correlation of neural responses with it's linear estimation

I am trying to understand the following fact from this article (page 13): How can single neurons predict behaviour Suppose I have a linear estimation of a stimulus: $ \hat{s} = \mathbf{w}^T(\mathbf{r}...
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1answer
134 views

identify a specific table in document image

I have some images of answered tests. I am working on recognizing the answers for the multiple choice questions on each test. A example: I think the problem can be separate into two sub-problems: ...
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1answer
342 views

Delay issue in time series prediction

I am having an issue using neural networks to predict time series. Some predicted data fits with the expected data, as bellow: (In black the real time series and in blue the output of my neural ...
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1answer
142 views

Artificial Neural Network Preprocessing in Real-Time Applications

I've come across an issue with my ANN when attempting to port my offline analysis to an online, real-time, application. I currently train my algorithm using an array of input data, number of ...
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1answer
500 views

FPGA vs DSP for MAC operations

I'm new to SP Stack Exchange . I am doing a project on implementing a handwriting recognition machine using Neural Networks in Real Time . The Image Processing Part involves convolving a 4*5 kernel ...
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3answers
347 views

Neural network with much less positive examples

I am developing a medical algorithm working on large amounts of data. However, as in many medical scenarios I have a lot of negative example and only a few positive ones. So any neural network I train ...