  Despite much research, traditional methods to pitch prediction are still not
perfect. With the emergence of neural networks (NNs), researchers hope to
create a NN-based pitch predictor that outperforms traditional methods. Three
pitch detection algorithms (PDAs), pYIN, YAAPT, and CREPE are compared in this
paper. pYIN and YAAPT are conventional approaches considering time domain and
frequency domain processing. CREPE utilizes a data-trained deep convolutional
neural network to estimate pitch. It involves 6 densely connected convolutional
hidden layers and determines pitch probabilities for a given input signal. The
performance of CREPE representing neural network pitch predictors is compared
to more classical approaches represented by pYIN and YAAPT. The figure of merit
(FOM) will include the amount of unvoiced-to-voiced errors, voiced-to-voiced
errors, gross pitch errors, and fine pitch errors.
