  Melody extraction is a vital music information retrieval task among music
researchers for its potential applications in education pedagogy and the music
industry. Melody extraction is a notoriously challenging task due to the
presence of background instruments. Also, often melodic source exhibits similar
characteristics to that of the other instruments. The interfering background
accompaniment with the vocals makes extracting the melody from the mixture
signal much more challenging. Until recently, classical signal processing-based
melody extraction methods were quite popular among melody extraction
researchers. The ability of the deep learning models to model large-scale data
and the ability of the models to learn automatic features by exploiting spatial
and temporal dependencies inspired many researchers to adopt deep learning
models for melody extraction. In this paper, an attempt has been made to review
the up-to-date data-driven deep learning approaches for melody extraction from
polyphonic music. The available deep models have been categorized based on the
type of neural network used and the output representation they use for
predicting melody. Further, the architectures of the 25 melody extraction
models are briefly presented. The loss functions used to optimize the model
parameters of the melody extraction models are broadly categorized into four
categories and briefly describe the loss functions used by various melody
extraction models. Also, the various input representations adopted by the
melody extraction models and the parameter settings are deeply described. A
section describing the explainability of the block-box melody extraction deep
neural networks is included. The performance of 25 melody extraction methods is
compared. The possible future directions to explore/improve the melody
extraction methods are also presented in the paper.
