Questions tagged [neural-network]

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Upsampling vs downsampling. Which to use when?

Downsampling reduces dimensionality of the features while losing some information. It saves computation. Upsampling brings back the resolution to the resolution of previous layer. My question is which ...
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80 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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49 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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48 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
155 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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17 views

Normalisation of CWT coefficients

I am trying to train a Convolutional Neural Network (CNN) model on an EEG measurement dataset consisting of 32 channels (i.e. 32 non-stationary signals at each time frame/window). In terms of ...
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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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13 views

Spectrogram vs MelSpectrogram

I am a university student in computer science and during a project, I run into the Mel Spectrogram. Doing some research, I found that the MelSpectogram is essentially a spectrogram where the distance ...
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23 views

Rule of thumb / Best practice for Audio (voice) data normalization for use in Classification

TRIVIAL QUESTION: I am currently working with some audio data of speech utterances. I am attempting to perform classification on the data based on the phonemes. This means that I manually label the ...
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12 views

Functioning of a continuous data stream processing neural network?

I've been searching with unfortunately no relevant results about something I have trouble to figure out: neural networks which process continuous data stream (audio, video, anything with a state ...
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1answer
76 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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37 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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1answer
53 views

Develop a simple image classification system by using Laplacian of Gaussian (LoG)

I am tring to write a simple CAD system to classify some images within two groups by using Laplacian of Gaussian (LoG). I am using scikit-image for this tasks and I want to use DNN in keras to train a ...
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1answer
61 views

Reconstruct images from PCA reduced dimensions with NN

I was reading this Medium post and I had the idea to reconstruct the original images with a convolutional neural network instead of applying the inverse transform method. The problem is that I don't ...
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16 views

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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261 views

Upsampling and downsampling signals as a preprocessing step for a neural network

I have audio data acquired from a 4 channels sensors array. As a preprocessing step for a neural network, I want to beamform and focus on the sound source. For higher resolution in the beamforming ...
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77 views

Image from Inverse FFT operation is sparse

I'm trying to train a deep nerual network that takes in input, an signal in the frequency domain, and attempts to learn a mapping to another signal in the frequency domain. Basically, the input to ...
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570 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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325 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 ...