A system using Monte-Carlo dropout for OOD detection and contrastive novel class discovery clusters unseen raga classes from unlabeled audio with 79-81% clustering accuracy on benchmark datasets.
Identification and Clustering of Unseen Ragas in Indian Art Music
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abstract
Raga classification in Indian Art Music is an open-set problem where unseen classes may appear during testing. However, traditional approaches often treat it as a closed set problem, rejecting the possibility of encountering unseen classes. In this work, we try to tackle this problem by first employing an Uncertainty-based Out-Of-Distribution (OOD) detection, given a set containing known and unknown classes. Next, for the audio samples identified as OOD, we employ Novel Class Discovery (NCD) approach to cluster them into distinct unseen Raga classes. We achieve this by harnessing information from labelled data and further applying contrastive learning on unlabelled data. With thorough analysis, we demonstrate the influence of different components of the loss function on clustering performance and examine how varying openness affects the NCD task in hand.
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Identification and Clustering of Unseen Ragas in Indian Art Music
A system using Monte-Carlo dropout for OOD detection and contrastive novel class discovery clusters unseen raga classes from unlabeled audio with 79-81% clustering accuracy on benchmark datasets.