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We propose a novel pitch estimation technique called DeepF0, which leveragesthe available annotated data to directly learns from the raw audio in adata-driven manner. F0 estimation is important in various speech processing andmusic information retrieval applications. Existing deep learning models forpitch estimations have relatively limited learning capabilities due to theirshallow receptive field. The proposed model addresses this issue by extendingthe receptive field of a network by introducing the dilated convolutionalblocks into the network. The dilation factor increases the network receptivefield exponentially without increasing the parameters of the modelexponentially. To make the training process more efficient and faster, DeepF0is augmented with residual blocks with residual connections. Our empiricalevaluation demonstrates that the proposed model outperforms the baselines interms of raw pitch accuracy and raw chroma accuracy even using 77.4% fewernetwork parameters. We also show that our model can capture reasonably wellpitch estimation even under the various levels of accompaniment noise.

DEEPF0: End-To-End Fundamental Frequency Estimation for Music and Speech Signals