  Extraction of the predominant pitch from polyphonic audio is one of the
fundamental tasks in the field of music information retrieval and computational
musicology. To accomplish this task using machine learning, a large amount of
labeled audio data is required to train the model that predicts the pitch
contour. But a classical model pre-trained on data from one domain (source),
e.g, songs of a particular singer or genre, may not perform comparatively well
in extracting melody from other domains (target). The performance of such
models can be boosted by adapting the model using some annotated data in the
target domain. In this work, we study various adaptation techniques applied to
machine learning models for polyphonic melody extraction. Experimental results
show that meta-learning-based adaptation performs better than simple
fine-tuning. In addition to this, we find that this method outperforms the
existing state-of-the-art non-adaptive polyphonic melody extraction algorithms.
