numpy,signal-processing,fft,ifft

The FFT of a real-valued input signal will produce a conjugate symmetric result. (That's just the way the math works best.) So, for FFT result magnitudes only of real data, the negative frequencies are just mirrored duplicates of the positive frequencies, and can thus be ignored when analyzing the result....

There are two problems with your computation: First, you evaluate the time domain filter coefficients on a very narrow time window, which truncates the filter and changes its characteristics. Second, you do not properly keep track of which indices in the time domain and frequency domain vectors correspond to time...

matlab,signal-processing,fft,ifft

Well, as the error suggests you have "too many output arguments". By looking at your code I believe that the problem is that audiowrite does not return any output arguments (have a look at http://www.mathworks.com/help/matlab/ref/audiowrite.html). You should use audiowrite(filename,audio_r,44100); instead. In any case, you should learn how to use the...

c,algorithm,signal-processing,fft,ifft

I strongly recommend to read this: http://stackoverflow.com/a/26355569/2521214 Now from the first look the offset and delta is used to: make the butterfly shuffle permutation you start with step 1 and half of the interval and by recursion you will get to log2(N) step and interval of 1 item ... +/-...

The inverse FFT can be obtained by making use of the forward transform: for (int i = 0 ; i < 512 ; i += 2) { fft_input[i] = (fft_input[i] >> 8); fft_input[i+1] = -(fft_input[i+1] >> 8); } fft_reorder(); fft_run(); // For complex data, you would then need to negate...

I apologize that the following is a bit messy. I did everything manually because I'm not sure how else to do it. First you need to know how MATLAB stores frequency domain data. Take the following example: N = 100; % number of samples Fs = 100; % sampling frequency...