indices1 = f(length(time)/2:end) > 94 %(high)range of frequencies aloud in reconstruction
indices2 = f(1:(length(time)/2)) < 7 %Low range
Those are not symmetric.
In order to get real-valued results from ifft(), the data that you are applying ifft() to must have conjugate symmetry: it must be of the form
[dc_component, some_coefficients, conj(fliplr(some_coefficients))]
for the case where the data is an odd number of samples.
In the case that the data is an even number of samples,
[dc_component, some_coefficients, real_value, conj(fliplr(some_coefficients))]
In this case, the first value of the second half of the vector must be real valued (no imaginary component)
Another way of looking at this is that if you were to rotate the array half way around, it would go
[coefficients_for_negative_frequencies, dc_offset, coefficients_for_positive_frequencies]
and the N'th entry before the dc_offset (e.g., negative 38.9 Hz) would have to be the conjugate of the N'th entry after the dc_offset (e.g., positive 38.9 Hz)
You are constructing your mask as if the second half of the array is for the high frequencies and the first half is for the low frequencies, but when you are working with fft data, the second half is for the negative frequencies. The low positive frequencies are the first quarter, the high positive frequencies are the second quarter, the high negative frequencies (reversed) are the third quarter, the low negative frequencies (reversed) are the fourth quarter
