# An Approach in Reproducing the Auto-Tune Effect Mentees: Dong-San Choi & Tejas Rawal Mentor: David Jun.

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An Approach in Reproducing the Auto-Tune Effect Mentees: Dong-San Choi & Tejas Rawal Mentor: David Jun

Overview Quick introduction to Auto-Tune Methodology in recreating the Auto-Tune effect Application

What is Auto-Tune? First created by Andy Hildebrand Corrects out-of-tune human sung pitches or instruments How? Autocorrelation & Phase Vocoder Demo using I Am T-Pain

Methodology Can be broken into two parts: 1) Pitch Detection - Autocorrelation 2) Pitch Shifting - Phase Vocoder

Pitch Detection There are many different approaches to estimate the pitch of a periodic signal We used the autocorrelation function to determine the fundamental frequency of our speech signal Autocorrelation refers to the correlation of a time series with its own past and future values. It compares a segmented section of a speech signal (human voice) with another segment from the same signal and calculates the time separation between them. Autocorrelation is a mathematical tool that helps find repeating patterns, such as: –A periodic signal which has been buried under noise –The fundamental frequency in a signal implied by its harmonic frequencies.

A Visual Representation

Pitch Shifting Pitch – 12 pure notes in an octave Each note corresponds to a frequency In-tune notes Out-of-tune notes Pitch shifting in Auto-Tune - use of a phase vocoder

The Phase Vocoder Use - to obtain a digital representation of speech Application - can modify basic speech parameters to permit alteration of time or frequency dimensions of a speech signal

The Phase Vocoder So… Inputs <= speech signal Outputs <= magnitude & phase What can we do with these parameters?

Our Approach

Application Auto-tune is a audio processing system use to enhance a singers vocal performance Auto-tune uses a phase vocoder unit to correct pitch and disguise vocal mistakes Many famous artists such as T-Pain and Kanye West currently use Auto-tune to perfect the vocals on their tracks

Thank You for your Time! Special thanks to the PURE program for the opportunity and our mentor, David Jun Any Questions/Comments?

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