Estimating fundamental frequencies in polyphonic music remains a notoriously difficult task in Music Information Retrieval. While other tasks, such as beat tracking and chord recognition have seen improvement with the application of deep learning models, little work has been done to apply deep learning methods to fundamental frequency related tasks including multi-f0 and melody tracking, primarily due to the scarce availability of labeled data. In this work, we describe a fully convolutional neural network for learning salience representations for estimating fundamental frequencies, trained using a large, semi-automatically generated f0 dataset. We demonstrate the effectiveness of our model for learning salience representations for both multi-f0 and melody tracking in polyphonic audio, and show that our models achieve state-of-the-art performance on several multi-f0 and melody datasets. We conclude with directions for future research.