### Omer Sayli (@omersayli)

Hi,If you have no signal processing background, these tasks would be very hard for you. Alternatively, If you have deep learning knowledge, you may use Neural Networks...

In NumPy, np.newaxis is used to add 'dimension' to the vector or matrix. The * is used for one-to-one multiplication of elements (not matrix product). I guess...

Hi,These are some quick search results you may be interested;Classification of Vehicles Based on Audio Signals using Quadratic
Discriminant Analysis and High Energy...

You introduce discontinuity -a spike like effect- at the instant where s2 starts just after s1. It is inevitable that spectral effects will occur, depending on the...

Hi,I finally got it, as you said, when using centered frequency result, we have to use ifftshift for the correct result instead of h = ifft(fftshift(sqrt(psd)));that...

Hi,
The imaginary part of h is very small compared to the real part, here their maximum values;
>> max(real(h)), max(imag(h))
ans =1.4761ans = 0.0099
Hence,...

Hi,For time invariance, you have to check whether if the shifted input signal also leads to the same output shifted by the same amount of time delay. i.e.y(t) =...

Hi,X value is complex exponential, x = exp(j*2*pi*1/2*(0:1000-1)); it will have a single frequency peak in the frequency domain.Even for a real x, if x is at the...

Actually we should normalize 'b' by b[1] in the loop (and after the loop, not by sum(b)), then we get the same result suggested without for loop;without for (added...

Trick for ff1 without loop solution is nice. Yes, the roots are too close, stopband bandwidth (2xFb/Fs ~ 0.002) is also too low compared to sampling rate.

Hi,even after b/sum(b), coefficients of b remain high; 1.0e+15 * Columns 1 through 8 0.0183 -0.2008 0.9831 -2.7972 4.9864 -5.3791 ...

1 column would mean one random variable. There won't be matrix for covariance for a single column data. I commented in the code, you have missed them. 3 columns...

I coded in Octave actually, I tested this in Matlabclear all; close all; clcN=100; % number of snapshots.M = 3; % Number of dimensions/variablesRzz = zeros(M, M);for...

Hi,
Could you check this code;
clear all; close all; clcN=100; % number of snapshots.M = 3 % Number of dimensions/variablesRzz = zeros(M, M)for j=1:N # z has...

Yes, this is what you want I think (I plot f0=5 case to show clearly that it works);
function saw = saw_wave_trig(fs, f0, s, m, w)
% fs = sample rate
% f0...

It works, (In your code, I noticed the wrong use of "t" in your calculations. I tried to change your code, you may check below )function saw = saw_wave_trig(fs,...

Think about calculating standard variance, you divide by the number of samples - 1 (for unbiased sample variance). Here N is your data sample number in the covariance...

Hi, No, there is no absolute value operation in covariance definition, but transposition and conjugation.

Hi,Since you use 5 samples for the 3 frequencies in complex exponential functions, you should use below for calculating covariance function (Resembling Matlab's...

Hi,In a speech (lets say a word utterance), simply vowels are vocalized (harmonic) parts, consonants are plosives, fricatives, nasals, liquids, and semivowels. You...

Hİ, Even if it is fixed, you apply in on sub-bands (there could be coefficients exceeding threshold or not). Coefficients are found for each level of tree, hence...

Hi,You know in Continuous wavelet Transform, you scale wavelets. In decimated discrete wavelet transforms, after each filtering you downsample the signal by 2 factor...

I'm glad that it helped.. Different libraries in a computing environment may provide same functionality sometimes, and you may also derive simple coefficients yourself...

Also you seem to get frequency response for output signal (fvtool(movAvgOut)), not filter response (MovAvg)

Hi,This is simple filter with window length of 5 (coefs 0.2). I got this using Python;
import numpy as npfrom scipy import signalimport matplotlib.pyplot as pltb...

Hi,I noticed that you use extremely high sampling frequency of 937kHz, while the EEG signal frequency content is below 100 Hz. Is it 937 Hz, is there a typo? Practically,...

or addding segments of gaussian white noises filtered with changing band pass filters could be tried also. You may check this.

Hi, A simple way is to use multiple chirp generators. YOu can control the bandwidth with the "boundary" chirps and also can add chirps in between these two. YOu...

Hi,Actually I have always used Wavelet analysis for "off-line" analysis. Also the formulation of wavelet analysis somewhat requires whole data, think about the scaled...

Hi Rick,Sorry, of course your Xc(m) is negative shift by fc (I stated sinosoids but missed total signal xc(t)-Xc(m)) My point was that as you have stated X(m)...

Hi sachinwannabe,Assuming communications signal (?), I want to remind , a baseband signal of interest with a much low frequency content (Bandwidth) -compared to...

Hi Rick,You say "Sequence xc(n) can have independent spectral components anywhere in the one GHz range of -0.5 Ghz -to- +0.5 Ghz.". Since we shift the spectrum to...

If you look at the tunelabguy's plots above, he confirms indeed fundamental frequency around 190 Hz and its 9 harmonics. YOur plot is also in this way, confirming...

Your plot also reflects peaks at harmonics of ~185 Hz. 380 Hz is one of them. (Fs=44100 Hz, good enough for resolution - not a reflection case here)

This is also a very nice approach, eliminating noise. I also found (also in this thread a sent) 380 Hz but there is a previous peak at 185-190 Hz. That is the desired...

I used autocorrelation (in Pyton, used librosa library) with the restriction that 120-240 Hz is the range for pitch find range.
import numpy, scipy, matplotlib.pyplot...

For human sound analysis, there are a lot of algorithms based on autocorrelation or frequency spectrum for "pitch estimation". spectrum.jpeg Looking at spectrum,...

You are right Rick , b = [1 0 0 0 -1] a = [1 -0.9] freqz_points = 512 [h, w] = freqz(b, a, freqz_points); [imp,t] = impz(b,a); h(1) = sum(imp) b_Order...

Hi Mr Lyons,First value of h is NaN as I have checked. Maybe that is the reason?Edit: (As noted by anamariatome also, that is the problem indeed, pole at z=1(f=0) causing...

Yes, it was big news indeed. Engineers solve 50-year-old puzzle in signal processingan interesting comment".. The authors are applying for an international patent...

Hi,you may check this book;Noise Reduction
in Speech
Applicationshttps://www.cin.ufpe.br/~hama/Arquivos/Noise%20Reduction%20in%20Speech%20Applications.pdfa python...

I think this is more like a 'pattern search-matching. Once match is found, delay is to be computed.Edit: I meant of course stretching-shortening signal of interest,...

I also thought that but the signal to be searched has to satisfy wavelet criterion to be used in a wavelet analysis as a 'wavelet'. If he can find a similar wavelet...

Hi,If I were to do that, I would first check in time domain the similarities-differences of the impulse responses. Then in frequency domain, I would check differences...

Hi,I use Python actively (forgetting even Matlab:) and found numpy/scipy libraries very decent and sufficient. Some info is here especially for DSP; https://docs.scipy.org/doc/scipy/reference/signal....Your...

Hi,Your updated version seems to calculate SNRs, I tried different f0 values. What was particular combination of parameters?

Hi, actually taking square values of the spectrum (sig_r_spectrum = (sig_r_dft(1:NFFT/2)).^2) is needed for finding powers, and multiplication by two is for...

Hi,If you really need a real-time operation, you may try DSP processor based solution. But unless you need that, I suggest to use Octave/Matlab, Python based analysis/solutions,...

HiStill you can apply rectify-Low pass filtering-ThresholdingYou may need to collect data to determine the threshold value. Also low pass filter cut off frequency...

Hi,Actually as fas as I have understood from your code, you are trying wavelet denoising, hence your question is related to the applicability of wavelet analysis...

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