Resample an array in real-time (application: pitch-bend) I'm working on a sampler software (that plays .WAV files, when notes are played on a MIDI keyboard).

In order to implement pitch-bend feature (i.e. you have a "pitch bend wheel" on the synth and you can turn this wheel to retune the sound in realtime, classical effect used in funk music),



we have to:


Retune: this is possible by resampling the array of the .WAV.
With Python, there is scipy.signal.resample or probably the same in numpy. 
But I heard there are algorithms optimized for audio repitching. What are the audio-optimized resampling algorithms?
[Secret Rabbit Code : http://www.mega-nerd.com/SRC/. Why is this important? How to implement it in Python, with numpy for example?]
Retune in realtime: indeed, we can play with the pitchbend-wheel very fast, thus, hundreds of resampling factor could be needed per second! How to do that?
How to resample an array with different resampling factors, in realtime?
Audio-optimized resampling is done by using anti-alias filtering and interpolation kernels with good stop-band attenuation and low pass-band ripple/distortion.  On current PC and mobile processors (the kind that can easily compute hundreds of transcendental functions per sample time), these filter and interpolation kernels can not only be precomputed and cached in poly-phase lookup tables, but for certain window types, (re)computed in real-time per sample.

Thus, resampling can be done in real-time by computing the needed interpolation kernels and interpolating each sample as needed at the required sample delay from the last for the current sample rate.

Note that changing the sample rate too quickly may result in FM modulation skirts that may need additional filtering to reduce aliasing artifacts.