  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.
