Recognizing Ornaments in Vocal Indian Art Music with Active Annotation

Ornamentations, embellishments, or microtonal inflections are essential to melodic expression across many musical traditions, adding depth, nuance, and emotional impact to performances. Recognizing ornamentations in singing voices is key to MIR, with potential applications in music pedagogy, singer identification, genre classification, and controlled singing voice generation. However, the lack of annotated datasets and specialized modeling approaches remains a major obstacle for progress in this research area. In this work, we introduce R\=aga Ornamentation Detection (ROD), a novel dataset comprising Indian classical music recordings curated by expert musicians. The dataset is annotated using a custom Human-in-the-Loop tool for six vocal ornaments marked as event-based labels. Using this dataset, we develop an ornamentation detection model based on deep time-series analysis, preserving ornament boundaries during the chunking of long audio recordings. We conduct experiments using different train-test configurations within the ROD dataset and also evaluate our approach on a separate, manually annotated dataset of Indian classical concert recordings. Our experimental results support the superior performance of our proposed approach over the baseline CRNN.

Novel Class Discovery for Open Set Raga Classification

The task of Raga classification in Indian Art Music (IAM) is constrained by the limited availability of labeled datasets, resulting in many Ragas being unrepresented during the training of machine learning models. Traditional Raga classification methods rely on supervised learning, and assume that for a test audio to be classified by a Raga classification model, it must have been represented in the training data, which limits their effectiveness in real-world scenarios where novel, unseen Ragas may appear. To address this limitation, we propose a method based on Novel Class Discovery (NCD) to detect and classify previously unseen Ragas. Our approach utilizes a feature extractor trained in a supervised manner to generate embeddings, which are then employed within a contrastive learning framework for self-supervised training, enabling the identification of previously unseen Raga classes. The results demonstrate that the proposed method can accurately detect audio samples corresponding to these novel Ragas, offering a robust solution for utilizing the vast amount of unlabeled music data available online. This approach reduces the need for manual labeling while expanding the repertoire of recognized Ragas, and other music data in Music Information Retrieval (MIR).

Explainable Deep Learning Analysis for Raga Identification in Indian Art Music

The task of Raga Identification is a very popular research problem in Music Information Retrieval. Few studies that have explored this task employed various approaches, such as signal processing, Machine Learning (ML) methods, and more recently Deep Learning (DL) based methods. However, a key question remains unanswered in all of these works: do these ML/DL methods learn and interpret Ragas in a manner similar to human experts? Besides, a significant roadblock in this research is the unavailability of ample supply of rich, labeled datasets, which drives these ML/DL based methods. In this paper, we introduce "Prasarbharti Indian Music" version-1 (PIM-v1), a novel dataset comprising of 191 hours of meticulously labeled Hindustani Classical Music (HCM) recordings, which is the largest labeled dataset for HCM recordings to the best of our knowledge. Our approach involves conducting ablation studies to find the benchmark classification model for Automatic Raga Identification (ARI) using PIM-v1 dataset. We achieve a chunk-wise f1-score of 0.89 for a subset of 12 Raga classes. Subsequently, we employ model explainability techniques to evaluate the classifier's predictions, aiming to ascertain whether they align with human understanding of Ragas or are driven by arbitrary patterns. We validate the correctness of model's predictions by comparing the explanations given by two ExAI models with human expert annotations. Following this, we analyze explanations for individual test examples to understand the role of regions highlighted by explanations in correct or incorrect predictions made by the model.