I am dealing with digital filtering of signals, both offline and in real-time. Typical filtering purposes are highpass filter or bandpass filter.
So far I worked on prerecorded signals (e.g.
wav files) so I could use a non-causal filter, such as Python's SciPy
filtfilt or MATLAB's
filtfilt, which produces zero-phase filtering (see What is zero-phase filtering and forward-backward filtering?). This has the desired effect of canceling any phase distortion. So far it worked very well off-line.
However, since according to theory this kind of filter is non-causal (e.g. the implementation of
filtfilt has a stage in which the signal is filtered backwards from its end to its start), it can't be used for "hard" real-time filtering (i.e. reading single sample at a time and immediately filtering it).
I ask if this non-causal filter can be used for "soft" real-time filtering, that is: I read a buffer of n samples, then filter the buffer using
filtfilt in the time-domain or by using its frequency response (which is purely real, obtained from the filter by the SciPy function
freqz) in the frequency domain (via FFT), and then I read the next buffer and so on.
Another question is what is the recommended way to apply this filter: in the time-domain via
filtfilt or in the frequency domain (multiplying the FFT coefficient $F(k)$ with the real positive frequency response $|H(k)|$)? The purpose is to have a minimally-distorted filtered signal and fast and computationally efficient filtering (to run in "soft" real-time).
Added in edit:
In my application a filtering in the frequency space is desired, and multiplying $F(k)$ by $|H(k)|$ seems to work, even in real-time. But multiplying by the absolute value is like performing zero-phase filtering, which by theory is non-causal. However, since I apply it buffer-wise, this seems to be the loophole. Am I correct?