Comparative Evaluation of YIN, MCOMB and Schmitt Pitch Detectors on One-Second Synthetic Sinusoidal Notes
Abstract
This study evaluates three pitch detection algorithms—YIN, MComb, and Schmitt Trigger—using one-second recordings of single musical notes and computer-generated sinusoidal signals. The use of short notes was motivated by applications such as automatic transcription, tuning systems, and real-time music analysis, which require algorithms to identify the fundamental frequency quickly and reliably. By isolating one-second single notes, we assessed pitch detection behavior under controlled conditions, without confounding factors such as overlapping notes, long-term dynamics, or tempo fluctuations. The tests included clean tones, signals with controlled distortions such as additional harmonic components or slight vibrato, background noise, and real saxophone recordings. Results showed that YIN achieved the highest accuracy under the tested synthetic and limited real-recording conditions, MComb showed strong performance on the evaluated real instrument recordings, and the Schmitt Trigger achieved high precision when noise levels were minimal. To address the limitations of individual methods, we implemented and tested a hybrid octave-aware weighted voting approach, which serves as a proof-of-concept combination strategy evaluated within this experimental setup.
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