signal-processing

Here's what I'm working with right now: for (int i = 0, numSamples = soundBytes.length / 2; i < numSamples; i += 2) { // Get the samples. int sample1 = ((soundBytes[i] & 0xFF) << 8) | (soundBytes[i + 1] & 0xFF); // Automatically converts to unsigned int 0...65535 int sample2 = ((outputBytes[i] & 0xFF) << 8) | (outputBytes[i + 1] & 0xFF); // Automatically converts to unsigned int 0...65535 // Normalize for simplicity. float normalizedSample1 = sample1 / 65535.0f; float normalizedSample2 = sample2 / 65535.0f; float normalizedMixedSample = 0.0f; // Apply the algorithm. if (normalizedSample1 < 0

Closed. This question is off-topic. It is not currently accepting answers. Learn more . Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 years ago . Where can I find a free, very quick, and reliable implementation of FFT in C#? That can be used in a product? Or are there any restrictions? torial AForge.net is a free (open-source) library with Fast Fourier Transform support. (See Sources/Imaging/ ComplexImage.cs for usage, Sources/Math/ FourierTransform.cs for implemenation) The guy that did AForge did a fairly good job but it's not commercial

The new Scipy v0.11 offers a package for spectral analysis. Unfortunately the documentation is sparse and there aren't many available examples. As a baby example, I'm trying to do period discovery of a sine wave. Unfortunately it predicts a period of 1 instead of the expected 2pi . Any ideas? # imports the numerical array and scientific computing packages import numpy as np import scipy as sp from scipy.signal import spectral # generates 100 evenly spaced points between 1 and 1000 time = np.linspace(1, 1000, 100) # computes the sine value of each of those points mags = np.sin(time) # scales

I am seeing a lot of literature in which they say that by using the fft one can reach a faster convolution. I know that one needs to get fft and and then ifft from the results, but I really do not understand why using the fft can make the convolution faster? FFT speeds up convolution for large enough filters, because convolution requires N multiplications (and N-1) additions for each output sample and conversely (2)N^2 operations for a block of N samples. Taking account, that one has to double the block size for FFT processing by adding zeroes, each block requires (2)*(2N)*log(2N) operations

A similar version of this question has been asked before ( how to compile fftw3 on iOS ) about previous versions of IOS and/or OSX, but I am unable to get fftw3 working on a actual IOS device. (Though it works fine in the simulator using the MacPorts "universal" distribution). If anyone has had any success using fftw3 please let me know how you were able to get it working! I am using OSX 10.7 and am trying specifically to run fftw on an iPad3 with IOS 5.1 installed. Thanks. 来源： https://stackoverflow.com/questions/10695053/compiling-fftw3-for-ios-5-1-on-osx-10-7

I am trying to make an audio spectrogram in C and am thinking about using the BASS library: http://www.un4seen.com . I searched over Google and here, but majority of examples are in C++. If any of you have any experience or resource regarding this, please help; it would be much appreciated. So this is what I want to do: Read from mic input. Sample the data. Apply Short time Fourier transform. Then take the magnitude and plot it at the certain frequency. Something like this: http://upload.wikimedia.org/wikipedia/commons/c/c5/Spectrogram-19thC.png If you can help it would be great. 来源： https:/