Human Voice Pitch Estimation: A Convolutional Network with Auto-Labeled and Synthetic Data
This work addresses pitch extraction for music and sound processing applications, but it is incremental as it builds on existing methods with data enhancements.
The researchers tackled pitch extraction from human singing voice in acapella performances by developing a specialized convolutional neural network, achieving efficacy across synthetic sounds, opera recordings, and time-stretched vowels.
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.