As you can tell from the sound clips in (Reference), all three algorithms work very well. The potential applications for these tools, especially in the recording industry, are boundless. With more reliance upon digital signal processing and less reliance upon recording a segment two or three or more times, clips may be produced much more quickly, for less money, and with better quality.
The shift synthesizer took by far the most time and effort to produce, mostly due to the difficulty of implementing a high quality harmonic detection algorithm. The randomizer ended up directly utilizing the pitch software, which provided even more incentive for us to improve our harmonic detection. Finally, the length changing program involved much more phase analysis than we had previously expected.
These triumphs and failures all provided great experience in dealing with some important issues in digital signal processing in general and analysis of human voice signals in particular. Over the course of developing the project, we dealt extensively with the characteristics of harmonics, the spectra of voiced and unvoiced fricatives, phase matching, and techniques for analysis and reconstruction of DFT vectors. All in all, the project was a huge success.
