signal-processing

问题 I analyzed the sunspots.dat data (below) using fft which is a classic example in this area. I obtained results from fft in real and imaginery parts. Then I tried to use these coefficients (first 20) to recreate the data following the formula for Fourier transform. Thinking real parts correspond to a_n and imaginery to b_n, I have import numpy as np from scipy import * from matplotlib import pyplot as gplt from scipy import fftpack def f(Y,x): total = 0 for i in range(20): total += Y.real[i]

问题 I'm trying to build a video chat program using Adobe Flex but there is a giant problem with echos. If the participants arn't using headsets, everything they say echos. Worse, they can actually create positive feedback loop of echos that won't end until the mics are muted. Has anyone found a solution for this on the Flex/Flash platform? My software is using the Speex codec and I've done my best to eliminate all buffering (i.e. it's a live stream and I set the buffer length to 0). The loop back

问题 I'm reading raw data from a mic and feeding into FFT. Two of the FFT libraries I'm trying (AForge and Exocortex.DSP) takes Complex numbers as input and gives Complex numbers as output. I'm trying to understand what complex numbers are. More specifically - how do I convert the raw audio data obtained from a microphone into complex numbers for processing in FFT? And how do I plot the output to a nice spectrogram (that is; reading the frequencies and amplitudes from the output)? Added bonus:

问题 I am performing the 2D FFT on a particular image and I get its spectral components. Now this image has been superimposed with another image to create periodic noise. The original image as well as the periodic noise version is shown below: Original Image Periodic Noise Image To filter this out, I used manual boxes that masked the components in the magnitude spectrum that are quite large relative to the other components as shown below. After this is done, I perform an inverse FFT, but I do not

问题 I am sampling a sine wave at 48 kHz, the frequency range of my sine wave can vary from 0 to 20000 Hz with a step of about 100 Hz. I am using a lookup table approach. So I generate 4096 samples for a sine wave for 4096 different phases. I think the general idea behind this to increment the step size and use different step sizes for different frequncy. So I do the following (pseudo code). But I am not sure how the step size is going to be related to the frequency I want to generate the samples

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 years ago . Google returns too much results, although SPUC caught my attention. Is there a standard recommended library like OpenCV for vision? The necessary features would be: Free Open Source filter design (Butterworth, Chebyshev, etc) FFT if possible, some speech processing features, like MFCC computation, although that

问题 I'm an experienced software engineer with some minor college DSP knowledge. I'm working on a smartphone application to process signal data, such as from the microphone (sampled at 44100 Hz) and the accelerometer (sampled at 32-50 Hz). My applications would be, for example, pitch detectors and so forth. I want to implement a low-pass filter (LPF) on the phone to remove aliased frequencies, particularly for the accelerometer, which has a low sampling rate. However, I am finding a contradiction

问题 Anybody know about the fastest method for calculating convolution? Unfortunately the matrix which I deal with is very large (500x500x200) and if I use convn in MATLAB it takes a long time (I have to iterate this calculation in a nested loop). So, I used convolution with FFT and it is faster now. But, I am still looking for a faster method. Any idea? 回答1: If your kernel is separable, the greatest speed gains will be realized by performing multiple sequential 1D convolutions. Steve Eddins of

问题 Anybody know about the fastest method for calculating convolution? Unfortunately the matrix which I deal with is very large (500x500x200) and if I use convn in MATLAB it takes a long time (I have to iterate this calculation in a nested loop). So, I used convolution with FFT and it is faster now. But, I am still looking for a faster method. Any idea? 回答1: If your kernel is separable, the greatest speed gains will be realized by performing multiple sequential 1D convolutions. Steve Eddins of

问题 I have a vector of data, which contains integers in the range -20 20. Bellow is a plot with the values: This is a sample of 96 elements from the vector data. The majority of the elements are situated in the interval -2, 2, as can be seen from the above plot. I want to eliminate the noise from the data. I want to eliminate the low amplitude peaks, and keep the high amplitude peak, namely, peaks like the one at index 74. Basically, I just want to increase the contrast between the high amplitude