Abstract: Gamaka (note ornamentation) is an essential element of Carnatic music. Earlier works in computer-generated gamakas focused on developing mathematical models of each gamaka, which fails to capture the intricate changes in pitch and thus does not sound natural. To address this challenge, this work approaches the synthesis of gamaka for kalpitha swaras (composed notes) in Carnatic music using a data-driven system. The model uses masked latent space representation in an auto-encoder architecture with features extracted using convolutional layers. It takes as input, the pitch contour extracted from symbolic data to generate a pitch contour with gamaka information embedded in it. The model is successful in synthesizing gamaka with nuances that closely follow the ground truth.