7
votes
Can deep neural networks achieve real-time video analysis?
Nvidia seems to have published some white papers comparing DNN inference performance between high-powered CPUs and (of course) Nvidia GPUs. (one example)
Ballpark seems to be that some systems can ...
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6
votes
Neural Networks and Complex Valued Inputs
The power of complex representations remains an open topic to me. I still do strive the understand Fourier transformations.
An underlying question is, to me: why would complex transformations be ...
- 31.6k
5
votes
Accepted
Spectrograms for neural nets
The "dimensions" of the spectrogram are not chosen based on where will the spectrogram be fed to but rather depend on your application. Therefore, it is key to understand the spectrogram itself first, ...
- 10.5k
5
votes
Is there a penis-detection demo similar to face-detection?
Is there a script / tutorial / demo for penis detection?
[...]
Fairly serious quesion, future of internet memes is at stake
Yes, there is.
Common Pattern Recognition techniques will be able to spot ...
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5
votes
Accepted
Downsampling audio for use in Machine Learning
Is this necessary if I only intend to use the data in the Neural Network toolkit provided by the repo I linked?
Yes.
Whether or not you are downsampling (instead of just decimating) has nothing to ...
- 10.5k
3
votes
Accepted
Pattern recognition in time series 4x3000 vector
Assuming your time series are the same length, take your data and produce spectrograms, $S_k$, for each row of the $4 \times 3000$ data matrix $D$ that you have. Since these individual time series ...
- 263
3
votes
Convolution and Cross Correlation on 2D Image
If as you said you understand well the 1-D convolution/cross-correlation functioning (the Wikipedia first graph explains it in a clear way), the 2-D version is very similar!
This website explains 2-D ...
- 368
3
votes
What is the best input for de-noising autoencoder for sound data?
As you mentioned MFCC features are one of the best features to represent audio as it captures both the time and frequency variations in the audio clip.You can get more details about MFCCS features in ...
3
votes
Accepted
Deriving the Langrangian interpolation polynomials in Cook-Toom convolutions
If you represent a second-order polynomial $s(x)$ with Lagrange polynomials $L_i(x)$ and interpolation points $\beta_i$, $i=0,1,2$, such that
$$s(x)=s(\beta_0)L_0(x)+s(\beta_1)L_1(x)+s(\beta_2)L_2(x)\...
- 87.2k
3
votes
Upsampling vs downsampling. Which to use when?
You should use the one you need for your problem, when you know which components of your signal are of interest to you.
Let's say you have in your electronic editing an ADC digitizing 40M samples per ...
- 107
3
votes
a neural network approach for FIR filter
Yes. The FIR filter model you're used to is a series of Neurons with weighted inputs, and a linear activation function.
In other words, a standard FIR filter is a neural network.
I mean, it's called &...
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2
votes
Accepted
Answering Machine vs. Human -- Neural Network Features Selection
I don't think that you would do this by feature extraction. Instead, I would train a many-to-one LSTM, which classifies the input sequence as -1 or 1. For you, it is easy to collect a huge amount of ...
- 5,435
2
votes
Can deep neural networks achieve real-time video analysis?
Here is a newly published paper and video example:
https://www.youtube.com/watch?v=w2iV8gt5cd4
http://arxiv.org/abs/1411.4389
- 21
2
votes
Neural Network: Spectrogram Dimension
Depends on on what kind of feature(s) you want your CNN trained. With 2D convolution, convolution in the F dimension of a T-F spectrogram will produce something like a real cepstrum, which has proved ...
- 35.1k
2
votes
What would the target matrix to train Neural Network?
Example with the following numbers (I use random numbers):
There are 6 possible classes (face expressions in your case).
You have 2000 examples (lets say 2000 photos of faces from which you know the ...
- 121
2
votes
Accepted
Does an equivalent transformation of a signal to a spectrogram image exist in which the phase information is part of the resulting image?
A homemade solution comes to my mind, but I don't know if it will work for you. I'll write it down anyway, since it may be helpful.
In MATLAB you can do:
...
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2
votes
Does an equivalent transformation of a signal to a spectrogram image exist in which the phase information is part of the resulting image?
If you create a volumetric (unflattened 3D) image, you can use 2 layers in the 3rd dimension to represent magnitude and phase, or real and imaginary components of a complex spectrogram output.
In a ...
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2
votes
Deep learning model for phone recognition - issues with dimension the model
Since next layer is fully connected it does not really matter what shape your pooling output would be. You have 14x100, you can rearrange them as 1x1400 as input for next layer, 1000 elements as ...
- 1,119
2
votes
Accepted
"Bi Directional" Kalman Filter - Kalman Filter for Smoothing
Anuar Y, Welcome to the DSP community.
What you're talking about is called smoothing.
Let me explain, assume we have samples $ {\left\{ x \left[ n \right] \right\}}_{n = 0}^{N - 1} $ and we want to ...
- 18.9k
2
votes
In Convolutional Neural Nets, what do convolutions look like?
The kernels used by a ConvNet are nothing but neural weights. You can think of them as a multilayer perceptron with some connections cut off and some weights restricted to be equal (weight sharing).
...
- 4,970
2
votes
Neural Networks and Complex Valued Inputs
Your question makes sense. The difference between a complex network and a regular network with twice the amount of channels, on a mechanical level, is the multiplication operation which ties pairs of ...
- 121
2
votes
How to train and test deep neural network using MFCC features?
You may want to try training your model on 2D arrays containing entire datasets (all 75 MFCC frames of each audio file). You can try recurrent or convolutional early layers in your DNN to handle the ...
- 35.1k
2
votes
Using STFT as an input to a Neural Net
Given a $M \times N$ STFT (spectrogram), use this as the input to a convolutional neural network. Do not flatten the spectrogram. Since your spectrogram will be complex, then you can use the magnitude ...
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2
votes
Is a neural network an adaptive filter?
or is it called a neural network because it is "fancy"?
Machine neural networks are called such because they deliberately emulate the functioning of biological neural networks, in an ...
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2
votes
Optimize window length (STFT) via gradient descent (in neural networks)
The easiest way is to take STFT using operators with autodiff support, e.g. via PyTorch. Then simply set the window as an updatable parameter, initializing as Hanning etc:
...
- 8,726
2
votes
Log of Filterbank Energies
Why is this done?
Mostly because human auditory perception works this way. The relationship between energy and perceived loudness is logarithmic. That is not only true for hearing but for most other ...
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1
vote
Accepted
Neural Network learning project based on 8 wave signals over 1 second at 1 sample every 10 ms ( hence 100Hz )
...a neural network that can decide wether a pattern produced by the movement of a hand near capacitive sensors is as expected, or random.
The neural network is supposed to learn himself how the ...
- 10.5k
1
vote
Neural Networks for rotated character (or shape) recognition
If you, during the successful training if the NN model, use data augmentation in the form of rotations and mirroring then they will become invariant to the model.
Namely it will have to extract ...
- 18.9k
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