Spectral centroid manipulations
It's been some time since I asked this question and after some work done on this subject, I think it's time to revisit it. It should be noted that
while the spectral centroid, pretty much like any other spectral feature, is calculated using the FFT magnitudes, and not raw FFT data, we don't 
have to manipulate the magnitudes directly. Instead, we modify FFT data, which in turn, affects the magnitudes. This makes the process
a lot easier than having to manipulate the magnitudes.

The spectral centroid is a single value (per FFT frame) so in the case of STFT we have as many centroid as we have STFT frames. The process of manipulating 
the spectral centroid is pretty straightforward. If, for instance, the spectral centroid is 600Hz and we wish to increase its value by 10% so that the new value is
660Hz, we would simply rearrange our frequency bins by a certain amount (a bin step). The actual calculation is qute simple 

const binStep = frameLenHalf - frameLenHalf/(1+(float)freqDelta/100); 
   //frequency delta % (e.g. 10%, 20%, -10%, -20% etc)

    //for increasing frequency
    float *pCfftLReal = new float[frameLen];
    float *pCfftLImg =  new float[frameLen];
    //for increasing frequency
    for(j=frameLenHalf-1;j&gt;binStep;j--){
            pCfftLReal[j] = pFFTReal0[j-binStep];
            pCfftLImg[j] = pFFTImg0[j-binStep];
                }
        }
    //for decreasing frequency
    for(int j=0; j&lt;frameLenHalf-1-binStep; j++){
            pCfftLReal[j] = pFFTReal0[j+binStep];
            pCfftLImg[j] = pFFTImg0[j+binStep];
                } 


Needless to say, this process suffers from rounding errors (primarily calculating the binStep), but in general, the larger the bin resolution, the larger the errors.
This can be alleviated by having a greater frame length (but for STFT this is usually undesirable). Other more complex approaches are also possible but I haven't investigated any of these.

Also, sometimes the results can be skewed if the amount of frequency shift exceeds the Nyqiust frequecy and we keep the original sample rate (upsampling would help here).

Before we can actually listen to our manipulated sound, an inverse FFT shoud be carried out on our modified data. 

What I've described here is just a rough, naive approach to spectral centroid manipulation. It should also be said that this kind of manipulation 
doesn't retain the original harmonics ratio, given that the frequency delta shift is applied equally on every bin. Keeping the harmonics ratio intact would require
calculating a new bin step for every frequency, which is only slightly more complex that the algo given above.

I've tried both approaches (with a constant bin step and a changing bin step) and perceptually, the first doesn't really change the original sound ( I guess 
this has to do with higher frequencies affecting timbre more than lower ones and the first approach changes the higher frequencies less than it does the lower ones).  

I'm not done with this yet, so I will update this post as soon as I have something interesting to share.