The melodic exploration of Indian Art Music is an emerging topic in the field of Music Information Retrieval (MIR), including several tasks to contribute to the understanding of this tradition. Ground-truth vocal melody annotations are an essential material to tackle this challenge, but the automatic extraction of these data from polyphonic audio signals is an unsolved task as of now. The state of the art on this topic is currently proposing high-performance data-driven pitch extraction models. Nevertheless, these models are exclusively developed using Western music data (pop, rock, blues and related styles) and therefore, the performance of these algorithms on Indian Art Music signals is significantly degraded. Furthermore, given the shortage of properly annotated vocal melody ground-truth for Indian Art Music, there are no works in the literature that propose pitch extraction methods or even re-train state of the art algorithms for this music tradition. In this work, we aim at overcoming this issue by addressing two main contributions: (1) The creation of a dataset of properly annotated vocal melody for Indian Art Music, and (2) The training, evaluation and testing of a state of the art vocal pitch extraction method to obtain a trained model that outperforms the actual proposals for Indian Art Music signals. Additionally, we review the related literature on Indian Art Music melodic exploration and on pitch extraction, to provide the basis for our research. We also contribute to the mirdata Python library by integrating loaders for Indian Art Music and other World traditional music datasets and promote the research on these cultures. We hope that the outcomes of this thesis contribute to the Indian Art Music melodic understanding and to the overcoming of the actual barriers produced by the shortage of data and methodologies for melody extraction of this rich and relevant musical culture.