Human Voice Pitch Estimation: A Convolutional Network with Auto-Labeled and Synthetic Data

In the domain of music and sound processing, pitch extraction plays a pivotal role. Our research presents a specialized convolutional neural network designed for pitch extraction, particularly from the human singing voice in acapella performances. Notably, our approach combines synthetic data with auto-labeled acapella sung audio, creating a robust training environment. Evaluation across datasets--comprising synthetic sounds, opera recordings, and time-stretched vowels--demonstrates its efficacy. This work paves the way for enhanced pitch extraction in both music and voice settings.