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The way MFCC's are always used is by feeding them into the classifier. This can be done on a frame-by-frame basis (12x1 vector), or by concatenating (12xN) - same as a spectrogram.
Thus for DTW, you must perform the classification by calculating the distance between 12D vectors. It's a Dynamic Time Warping, so the difference must be calculated between ...
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If you are looking for a good explanation of some of the methods used in projects like GRAIL look here:
Back to the Future of Hand Writing Recognition
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Surprised? DARPA (GRAIL project?, et.al.) supported handwriting recognition research circa 3 decades earlier, and on mainframes less powerful than a PalmPilot 1000.
Hawkins talks a bit about implementing Graffiti in a Computer History Museum oral history.
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It seems most of the data is in Graffiti (Palm OS) - Wikipedia.
It seems the technology is related to Unistrokes technology (U.S. Patent 5,596,656, granted in 1997).
Since the model relies on single stroke and using symbols that are exceptionally well separated from each other graphically I would be assume parameterizing the stroke would be a great way to ...
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p and q - order of the moments, it is the analogue of the mechanical moments (i.e. of force or inertia). Image moments are very basic properties of image, invariant to rotation, could be used as simple descriptor.
Example,
Moment of inertia Ix= I2 = mass *x_coordinate^2.
Image moment Ix2 = intensity * *x_coordinate^2.
http://en.wikipedia.org/wiki/Moment_(...
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In music theory, an octave is an interval in frequency, from a frequency $f$ to frequency $2f$. For example "an octave higher" means "twice the frequency". Expressed as wavelength inversely proportional to frequency, $\lambda \propto \frac{1}{f}$, an octave would be the interval from a $\lambda$ to $\frac{1}{2}\lambda$. In the SIFT paper'...
2
The openSMILE audio feature extraction toolkit may be able to provide the functionality you desire, where the input is a .wav file and the output extracted audio features. See: http://audeering.com/technology/opensmile/
openSMILE provides a command line executable that is coupled with a configuration file that defines the features to be extracted. The ...
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Have a good read of various features that can be extracted from popular audio analysis libraries like librosa: https://librosa.github.io/librosa/feature.html
Fundamental frequency (F0) can definitely be extracted. This itself a whole subfield, so google and previous SO questions are your best starting point after reading through what a pitch tracker looks ...
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I dont think there is a common framework for defining all those operators. Each of them were developed by different people, at several moments of the history. They are being integrated day by day onto a common discipline called DSP.
As clarification, an operator (like a moment or any other) and the "redundancy" of a signal are very different and unrelated ...
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Because wo want to get the centroid of the image(a block/patch) by the intensity.
m00:p = q = 0,sum the intensity matrix.
m10:p =1,q = 0,sum of the x-direction.
m01:p = 0,q = 1,sum of the y-direction.
(m10/m00,m01/m00) is the centroid.
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Try one of these algorithms for feature selection:
BLogReg
CFS
Chi Square
FCBF
Fisher Score
Gini Index
Information Gain
Kruskal-Wallis
mRMR
Relief-F
SBMLR
T-test
SPEC
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Your understanding of the convolution process is correct. However, note that in the convolution the kernel is first mirrored before the dot product. Though, for a symmetric kernel this does not matter.
Understanding the convolution in your way, of finding specific features, is a similar interpretation as that of a Matched filter. Furthermore, using the ...
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Well, I don't know whether it'll actually help you – you just said it would!
Now, in any case, using an algorithm to extract features from a signal that mimics or resembles human perception should inherently give those feature vectors a higher "mathematical" resemblance when a human would find the original signals similar, too.
This is obviously what you ...
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This project Speech Signal Processing Toolkit (SPTK) provides several features you are looking for. Here is a good wrapper around it https://github.com/r9y9/pysptk
This wrapper is using a slightly different version of SPTK, but installation is straightforward.
pip install pysptk
1) I would use kaldi or another speech recognition package on phones output ...
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I think a high-level explanation is the right thing to give here:
For recognition of a producing process (in this case: speech) from a signal (in this case: audio), you need to find a mapping from signal to a set of features.
The closer (correlated) these features are to the parameters of the producing process (here: voicing utterances), the more ...
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Well the state of the art performance on such tasks is achieved by deep neural networks, and especially, the convolutional ones (CNNs) set you free of extracting hand crafted features. The network learns those features as well as the weights. This way you avoid the hustle or the rots o'ruck. For sequential data input, such as audio signals, recurrent neural ...
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Analyzing signals per segments, with proper windowing, is a way to cope with non-stationary in audio samples. With full-size analysis, features can get mixed. Segment-splitting is thus at play in many algorithms (mp3, shazam).
The length of window is often a matter of trade-offs, between data information and computing advantages:
signal sampling (window ...
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There is no reason why your piezo shouldn't be able to produce a bipolar output, if you use proper biasing and/or preamp. See for example https://www.homemade-circuits.com/diy-contact-mic-circuit/
Working with the magnitude only is probably a non-starter. $y = |x|$ is a highly non-linear operation and will dramatically change your spectrum. For example a ...
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Another approach which might be appropriate in your Bayesian approach (Filter Particle) would be Mean Vector + Covariance Matrix.
Even for that you could employ your idea of weighing (Which is reasonable).
I would pay attention to the color space you're working in.
Euclidean Distance isn't working (At least not well) in RGB.
Example in CIE LAB
The mean ...
1
Assuming you have epochs/segments of data. For this kind of signals, it is a safe approach to extract features using wavelet representations.
Using FFT might work as well, but I dont know how problematic would be the stationarity assumption in this kind of applications. Besides, FFT estimates for this kind of signals is sometimes very noisy. If you insist ...
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It's not particularly important since the different bases just result in a different overall scale factor, but the basic shapes are the same.
This being said, any actual numbers need to be defined with a specific bases otherwise it's ambiguous. The most common is the $dB$ which (for power) is defined as
$$L = 10 \cdot log_{10}(P/P_0)$$
i.e. it uses base ...
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VTLN is pretty old technology. There is a fast linear VTLN or basis fMLLR which is about the same thing. These days everyone is doing neural networks though, they provide more advanced speaker adaptation, at the same time they could be pretty efficient with quantization and proper weight pruning.
For example you can check DOMAIN AND SPEAKER ADAPTATION FOR ...
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TL;DR the x-axis is time and the y-axis is coefficients which means they have no units.
Let's go through the process of manufacturing the MFCC features while emphasizing measure units tracking. We start from a sampled audio signal $s(n)$ with amplitude units on the y-axis and time units on the x-axis.
First step is to frame the audio signal. An audio ...
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How this 36*1 came and how we calculated it?
HOG is an algorithm which:
works on a portion of the image, called "detection window";
divides the "detection window" in a certain number of cells;
associates an histogram (of oriented gradients) to each cell.
Each histogram has N orientation bins. WLOG, here, we'll consider N=9);
"Normalization step" (to make ...
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I tried PHOT (suggested by @applesoup) using the unofficial implementation given under https://github.com/thinkng but it did not work for these image sets. Maybe one needs to further investigate or tweak the algorithm a bit.
First, if you have sufficient amount of data, I do not believe that one could easily outperform a good deep architecture in the task ...
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In the case of switched analog POTS phone lines, the audio response is shaped by the limited frequency response of the carbon microphone and the speaker in the handset.
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I think the time history of the wave file is enough to obtain all sorts of features. There are mathematical formulae to extract the features from a given time series.
Some important features are:
Time Domain Audio features
(a) Short-term Energy, (b) Zero Crossing Rate, (c) Entropy of Energy, (d) Basic Statistics (short term mean and standard deviations)...
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A possible way to approach the problem would be to extract the maximum values of the discrete circular convolution of each pre-normalized sequence with an NXN matrix formed by the elements of an orthonormal base of the Nx1 sequence space.
The output of this step would be a Nx1 vector that would contain a measure of the similitude of each sequence with the ...
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A relatively easy way to do that would be to write MATLAB/GNU Octave/Python scripts for data extraction (the latter two being open-source). A major advantage is that a lot of the work can be done automatically, provided you script it. Depending on what you would like to do, a lot of features may exist in form of built-in functions.
While GUI software for ...
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