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On the top of this answer, you can see a section of updated links, where artificial intelligence, machine intelligence, deep learning or and database machine learning progressively step of the grounds of traditional signal processing/image analysis/computer vision. Below, variations on the original answer.
For a short version: successes of convolutional ...
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First, there is nothing wrong with doing grad work in image processing or computer vision and using deep learning. Deep learning is not killing image processing and computer vision, it is merely the current hot research topic in those fields.
Second, deep learning is primarily used in object category recognition. But that is only one of many areas of ...
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By long shot it is doable - to what extend? You will see. This task of environmental sound classification is not very well studied. Also choice of machine learning paradigm is crucial - statistical approach or maybe binary classifier? You can start with GMM's, ANN's and SVM's - I opt for GMM's and ANN's.
Yes, most of people are using MFCC's because they are ...
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Some Features:
Mean.
Variance.
Skewness.
Kurtosis.
Dominant 3 frequencies in the DFT.
Energy of the 3 dominant frequencies.
Max Value.
Min Value.
Median.
Total Variation.
Usually I'd compute them in running windows.
Another great information is the Histogram of the Derivative.
Or just all the above of the Derivative.
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No Deep Learning isn't killing Image Processing. You need huge datasets and lots of computational resources to do deep learning. There are plenty of applications where it is desirable to be able to do image processing with less computational load and smaller memory footprints and without having access to huge databases. Some examples are mobile phones, ...
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Today we had a discussion with a friend of mine. It was a rainy day here in Munich, while a large portion of Europe was having a kind of sunny atmosphere. People were sharing photographs in social media, where they were in nice summer dresses, wandering around the seas. She was annoyed with this situation and turned to me and asked: "Could you write a ...
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From feature extraction to learning the desired result, deep learning algorithms can act as full pipelines for solving tasks at hand. End-to-end learning usually refers to omitting any hand-crafted intermediary algorithms and directly learning the solution of a given problem from the sampled dataset. This could involve concatenation of different networks ...
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The short answer is, No. DL can recognize a mug in a photo, but this doesn't kill signal processing in anyway. That said, your question is quite relevant in these troubled days. There is a nice panel discussion on the subject, featuring Stephane Mallat, etc., here.
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It can be done very easily with the scikit-learn. Examples are easy to find on their website, i.e. here. In my opinion it is the best way to go.
Modified code example from the above link:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets.samples_generator import make_blobs
########################...
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In addition to the features mentioned so far I would like to mention measures of complexity such as:
Shannon Entropy
LZ Complexity
Fractal Dimension
There are also Fourier Descriptors (as hinted by Drazick already) and their equivalent in Wavelet Analysis and of course simple histogram bins which would return how frequently each gear is engaged en route.
...
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Data engineering is still used in machine learning to preprocess and select the data fed to DNNs to improve their learning time and their evaluation efficiency. Image processing (the stuff between the camera sensor and the RGB/etc. bitmaps fed to the DNNs), a form of data engineering, is still needed.
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There are, a few discrepancies that might be making a difference here. My suggestion would be to edit the question for clarity. There are quite a few assumptions that lead to non-straightforward thinking about the problem which I have tried to address to an extent and I would be happy to modify the response in light of more information.
In machine ...
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There is a package called FukuML.
In their description (Version 0.4.1) they write:
Support Vector Machine
Primal Hard Margin Support Vector Machine Binary Classification Learning Algorithm
Dual Hard Margin Support Vector Machine Binary Classification Learning Algorithm
Polynomial Kernel Support Vector Machine Binary Classification Learning Algorithm
...
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General Idea
The general idea of Principal Component Analysis (PCA) is as following (Intuition over formalism):
Given a set of points in space (Inner Product Space) find a set of vectors (Directions) which are uncorrelated which span the data in the most energy preserving manner.
The tricky part is explaining "most energy preserving manner".
So we're ...
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There are many way to tackle this:
Time - Frequency Analysis
Classic choice would be a spectrogram but probably a Fourier Synchrosqueezed Transform would do a better job (Have a look at even more advanced approaches ssqueezepy - Synchrosqueezing, wavelet transforms, and time frequency analysis in Python by @OverLordGoldDragon).
Parameter Estimation
Methods ...
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The language choice depends on many factors.
For instance, are you after developing low level features of DNN or using existing building blocks?
Most advanced and popular Deep Neural Networks (DNN) Frameworks are nativly integrated into Python though they are mostly implemented using different low level language (C++ mainly).
Those include PyTorch and ...
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I would use the SVD (Singular Value Decomposition).
By looking at the Singular Values I'd determine which vectors spread the data and which spread the noise.
Practically, they both do both, but if we speak which are dominant, this would be a great starting point.
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Non-verbal Audio (let alone environmental) seems to be the little brother to main stream machine learning media types like images, speech, text.
To answer your question is it possible to train a network to identify a given sound? Yes, it is! But it's hard for all the same reasons machine learning is hard.
However what's really holding Audio back, and why ...
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A thorough understanding of signal processing (along with linear algebra, vector calculus, mathematical statistics etc.) is imo indispensable for non-trivial work in the field of deep learning, especially in computer vision.
Some of the high impact papers in deep learning (now that most of the low hanging fruit have been picked) evince a good understanding ...
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I think you have a matrix.
Each Row / Column is a descriptor vector of a point in the image.
Just like having features, let's say M features, and each point has M values corresponding to M features.
So each element in the descriptor vector is a value of specific feature for this point.
And yes, if you have M = 128 Features and 1000 points you'll get a ...
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Types of signals:
According to their range set (values): Real Valued, Complex valued ;
According to their dimensions: Scalar, Vector ;
According to their values: Continuous Amplitude, Quantized ;
According to their domain set (arguments): Continuous-time, Discrete-time :
According to their mappings: Deterministic, Stochastic (Random) ;
According to their ...
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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 do with classification performance but rather, it is to preserve (as much as possible) the information contained in the signal.
When changing the sample rate ...
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Band-pass filtering with cut-off frequencies of 300 Hz and 3400 Hz should result in a good approximation. Try with a Chebychev filter or order not more than 6.
Then you may need to downsample your audio to 8000 samples per second, which is the standard for telephony.
P.S. The actual cut-off frequencies (especially the 3400 Hz) may be different according to ...
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By deep learning, I'll assume you mean neural network. To develop a neural network, you'll need labeled data. This means you need a bunch of example inputs ($r$) and outputs ($X$). Once you have that, you need to build a network. As far as I can tell, currently the guidelines for building these things are very loose and ad-hoc. There is general consensus on ...
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This is an open discussion.
In my opinion, any field we couldn't figure near optimal solution (And in most cases we only have Linear Optimal solution) using Deep Learning will replace classic methods given enough data to work with.
The power of data driven features vs. intuitive (Though mostly right yet never cover everything) will prevail.
I don't see it as ...
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Firstly, I am confused if I am supposed to filter my signals to get rid of any frequencies above the Nyquist frequency. My sampling frequency is 32Hz and my time series is somewhat noisy and has some artifacts. I am also unsure of which filter to select for this.
That ship has sailed.
Let $S=\left\{\left.\alpha e ^{i(\omega t+\varphi)}\right|\alpha > 0, ...
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I've kind of grouped your subjects into larger overall subjects.
Note that there's a lot of overlap here, with the possible exception of actually making it work in a microprocessor (except -- in my opinion the best person to implement something is someone who understands it. So -- overlap).
Specifically, you could claim that it's all applied math. Or all ...
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Wouldn't this be equivalent to discarding all those frequencies in the input layer?
No, it won't for 2 facts:
Just like a CNN isn't a linear regression due to having non linear function in between. So the Activation Layers on the frequency domain mean things are not propagated linearly in the forward pass.
The filters are adaptive (Learned). Namely each ...
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