  We propose a novel pitch estimation technique called DeepF0, which leverages
the available annotated data to directly learns from the raw audio in a
data-driven manner. F0 estimation is important in various speech processing and
music information retrieval applications. Existing deep learning models for
pitch estimations have relatively limited learning capabilities due to their
shallow receptive field. The proposed model addresses this issue by extending
the receptive field of a network by introducing the dilated convolutional
blocks into the network. The dilation factor increases the network receptive
field exponentially without increasing the parameters of the model
exponentially. To make the training process more efficient and faster, DeepF0
is augmented with residual blocks with residual connections. Our empirical
evaluation demonstrates that the proposed model outperforms the baselines in
terms of raw pitch accuracy and raw chroma accuracy even using 77.4% fewer
network parameters. We also show that our model can capture reasonably well
pitch estimation even under the various levels of accompaniment noise.
