Why Autocorrelation Outperforms FFT for Browser-Based Erhu Tuner Accuracy

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A developer building a browser-based tuner for the Erhu, a traditional two-stringed Chinese bowed instrument, found that standard FFT-based pitch detection failed due to the complex noise produced by bowing. Bow friction generates high-frequency scratchiness, transients, and strong harmonics that cause FFT algorithms to misread the fundamental pitch. To solve this, the developer switched to a time-domain autocorrelation algorithm, which identifies pitch by measuring how a waveform repeats over time rather than analyzing frequency peaks. Because bow noise is random and non-periodic, autocorrelation naturally filters it out and locks onto the true fundamental frequency with sub-1Hz precision. This approach proved essential for fretless instruments like the Erhu, where even a fraction of a Hertz in tuning error can undermine a player's intonation.

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