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A notable example of Auto-Tune-based pitch correction is the Cher effect, so named because producer Mark Taylor originated the effect in her 1998 hit song "Believe". [4] The effect has been used by composer John Boswell for his Symphony of Science and Symphony of Bang Goes The Theory (a BBC science show) mash-ups.
Later in the 2000s, T-Pain used Auto-Tune extensively, further popularizing the use of the effect. [22] He cited the new jack swing producer Teddy Riley and funk artist Roger Troutman's use of the talk box as inspirations. [18] T-Pain became so associated with Auto-Tune that he had an iPhone app named after him that simulated the effect, "I Am ...
Frequency domain, polyphonic detection is possible, usually utilizing the periodogram to convert the signal to an estimate of the frequency spectrum [4].This requires more processing power as the desired accuracy increases, although the well-known efficiency of the FFT, a key part of the periodogram algorithm, makes it suitably efficient for many purposes.
In the standard guitar-tuning, one major-third interval is interjected amid four perfect-fourth intervals. In each regular tuning, all string successions have the same interval; all-fifths tuning has perfect fifths between all string successions.
Two machines were used for the vocal editing in "Hollywood". Madonna preferred the Antares Auto-Tune plug in, while Ahmadzaï chose an AMS pitch shifter. [8] Madonna wanted Auto-Tune because she wanted "Hollywood" to have a more dance-like feel to it, although Ahmadzaï was against it. [8] Describing the recording of the vocals, Ahmadzaï said:
In Paris, Ahmadzaï worked obsessively to complete the recording of "Impressive Instant", applying his characteristic sound mangling. He used the Antares Auto-Tune plug-in set for the pitch correction. The audio processor kept the characteristic of Madonna's vocals, and she sang a little out of tune and vibrato. [5]
Piano tuning became a profession around the beginning of the 1800s, as the "pianoforte" became mainstream. [6] Previously musicians owned harpsichords, which were much easier to tune, and which the musicians generally tuned themselves.
These algorithms differentiate themselves by their ability to autonomously adjust their parameters in response to the problem at hand, enhancing efficiency and solution quality. This self-tuning capability is particularly important in complex optimization scenarios where traditional methods may struggle due to rigid parameter settings.