# Tag Info

11

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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It it based on the definition of Central Moments on 2D grid given in the Wikipedia Page. $${\mu}_{pq} = \int\limits_{-\infty}^{\infty} \int\limits_{-\infty}^{\infty} (x - \bar{x})^p(y - \bar{y})^q f(x,y) \, dx \, dy$$ Since we're dealing with 2 random variables, a multiplication which the powers sum to 3 can be achieved in 4 different methods. It's up ...

5

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 ...

5

What other "modern" methods exist for accurate phase extraction? Unless the frequencies in the signal are phase locked to your sampling clock an FFT is not a great way to determine either frequency or phase of a sinusoidal component. In many cases a Phase Locked Loop (PLL) or Delay Lock Loop works much better. You can use an FFT to quickly ...

4

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_(...

3

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 ...

3

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.

3

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'...

3

A feature is a number that describes one aspect of a signal. Signals can be very complex, and the simplest analysis tools (like a time plot, a spectrum, or an energy measurement) don't tell you everything; in fact, for specific types of analyses, they almost don't tell you anything useful. So, features are designed to describe very specific aspects of a ...

2

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 ...

2

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

2

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 ...

2

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 ...

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 ...

2

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 ...

2

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 ...

2

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 ...

2

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 ...

2

Do an FFTShift (rotate the data halfway) before doing an FFT for phase analysis. That will re-reference the measured phase to the center of your data, not to potential circular discontinuities at the ends. Any discontinuities near the phase reference point (due to any non-integer-periodic-in-aperture frequencies) will corrupt phase interpolation. Do not &...

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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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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