Neural networks for supervised pitch tracking in noise
Kun Han, DeLiang Wang · 2014
Determination of pitch in noise is challenging because of corrupted harmonic structure. In this paper, we extract pitch using supervised learning, where probabilistic pitch states are directly learned from noisy speech. We investigate two alternative neural networks modeling the pitch states given observations. The first one is the feedforward deep neural network (DNN), which is trained on static frame-level features. The second one is the recurrent deep neural network (RNN) capable of learning the temporal dynamics trained on sequential frame-level features. Both DNNs and RNNs produce accurate probabilistic outputs of pitch states, which are then connected into pitch contours by Viterbi decoding. Our systematic evaluation shows that the proposed pitch tracking approaches are robust to different noise conditions and significantly outperform current state-of-the-art pitch tracking techniques.