Convolution in frequency space with fft and ifft -> num output samples? Let's say you had two 1 second sound samples at 44.1khz, and you wanted to convolve them in frequency space.

You FFT each one to get two arrays of complex numbers that each have 44100 items in them.

You then multiply each bin...
for i = 0 to 44099
c[i] = A[i] * B[i]

Then it's time to turn it back into time domain samples.  If you do an ifft you'll get 44100 time samples out, but when you convolve these two audio samples in time space, it'll give you 88199 samples out (44100+44100-1).

Obviously getting 44100 samples out is incorrect.  At what part in the process did I go wrong? (:

Thank you!!
What you do is called circular convolution but you want "linear" convolution: Say you have two signals of length $N$ and $M$. Append zeros to both time-domain signals, so that their length is $L=N+M-1$, then calculate the $L$-DFT of both signals, multiply them in frequency-domain and convert the result back to time-domain.

In your case, you should append 44099 zeros to each signal before transforming it to the frequency domain. The result will then be 88199 samples long.

If the available FFT is shorter than 88199 you have to divide the signal into shorter blocks and apply the overlap add method.