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Gerald Leung
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Implementation Goal of Phase Vocoder Spectral Analysis and Manipulation Matlab Implementation Result Discussion and Conclusion
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Time Stretch ◦ Unwrap phase and calculate the Magnitude and Phase increment per sample in analysis grid ◦ Interpolate the synthesis phase response using phase increments calculated from analysis grid in previous step ◦ Re-construct the audio signal using a weighted sum of cosine functions using the interpolated synthesized magnitude and phase as cosine parameters *** IFFT Operation ***
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FFT of a window function is represented as a series of Sine and Cosine Functions http://zone.ni.com/devzone/cda/tut/p/id/3342
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Given the interpolated phase and magnitude, we can re-construct the signal from frequency domain for each window segment Overlap add the interpolated window segment using the same hope size window in the analysis grid
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Similar Concept as Time Stretching ◦ Take FFT of each window segment and calculate the magnitude and phase increment per sample (FFT bin) ◦ Multiply the phase increment by a transposition factor ◦ Interpolate the synthesis signal using transposed phase increments from previous step ◦ Re-construct the signal from the frequency domain using weighted sum of cosine functions with interpolated phase and magnitude as parameters
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Zero-phase every FFT bin Take the IFFT of the magnitude of FFT bin for signal re-construction
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Same concept as Robotization Randomize the phase instead
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Maintain loud parts of the signal Attenuate low parts of the signal Modify the magnitude, maintain the phase grain = in_signal(pin+1:pin+WLen).*w1; f = fft(grain); r = abs(f); ft = f.*r.^2./(r+coef);
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Not Completely free of artifact free ◦ Mathematically impossible to express the exact spectrum ◦ Phasiness, reverbation etc
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Large windows causes “smearing” effect Smaller windows ◦ Less frequency resolution ◦ Minimum frequency is more restricted Less bass
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