  We propose a model to estimate the fundamental frequency in monophonic audio,
often referred to as pitch estimation. We acknowledge the fact that obtaining
ground truth annotations at the required temporal and frequency resolution is a
particularly daunting task. Therefore, we propose to adopt a self-supervised
learning technique, which is able to estimate pitch without any form of
supervision. The key observation is that pitch shift maps to a simple
translation when the audio signal is analysed through the lens of the
constant-Q transform (CQT). We design a self-supervised task by feeding two
shifted slices of the CQT to the same convolutional encoder, and require that
the difference in the outputs is proportional to the corresponding difference
in pitch. In addition, we introduce a small model head on top of the encoder,
which is able to determine the confidence of the pitch estimate, so as to
distinguish between voiced and unvoiced audio. Our results show that the
proposed method is able to estimate pitch at a level of accuracy comparable to
fully supervised models, both on clean and noisy audio samples, although it
does not require access to large labeled datasets.
