# Recognizing Ornaments in Vocal Indian Art Music with Active Annotation

arXiv: https://arxiv.org/abs/2505.04419 · HTML: https://mlsanity.com/paper/2505.04419/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 48 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 18 of 18)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://mlsanity.com/paper/1608.08242/) (2016)
- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [Temporal Action Segmentation: An Analysis of Modern Techniques](https://mlsanity.com/paper/2210.10352/) (2022)
- [Sound Event Detection: A Tutorial](https://mlsanity.com/paper/2107.05463/) (2021)
- [Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection](https://mlsanity.com/paper/1702.06286/) (2017)
- [Singing Voice Conversion with Disentangled Representations of Singer and Vocal Technique Using Variational Autoencoders](https://mlsanity.com/paper/1912.02613/) (2019)
- [Deformable CNN and Imbalance-Aware Feature Learning for Singing Technique Classification](https://mlsanity.com/paper/2206.12230/) (2022)
- [Explainable Deep Learning Analysis for Raga Identification in Indian Art Music](https://mlsanity.com/paper/2406.02443/) (2024)
- [Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers](https://mlsanity.com/paper/2210.17367/) (2022)
- [R-CRNN: Region-based Convolutional Recurrent Neural Network for Audio Event Detection](https://mlsanity.com/paper/1808.06627/) (2018)
- [Balanced Deep CCA for Bird Vocalization Detection](https://mlsanity.com/paper/2211.09376/) (2022)
- [Scream Detection in Heavy Metal Music](https://mlsanity.com/paper/2205.05580/) (2022)
- [PrimaDNN': A Characteristics-aware DNN Customization for Singing Technique Detection](https://mlsanity.com/paper/2306.14191/) (2023)

## Built on by (most cited first, 5 of 5)

- [Learning from Limited Labels: Transductive Graph Label Propagation for Indian Music Analysis](https://mlsanity.com/paper/2601.03626/) (2026)
- [Learning to Discover: A Generalized Framework for Raga Identification without Forgetting](https://mlsanity.com/paper/2601.18766/) (2026)
- [Automatic Detection and Analysis of Singing Mistakes for Music Pedagogy](https://mlsanity.com/paper/2602.06917/) (2026)
- [TCPα: Margin-Controlled Confidence estimation for reliable Music Information Retrieval](https://mlsanity.com/paper/2608.20326/) (2026)
- [Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations](https://mlsanity.com/paper/2610.15350/) (2026)

## Abstract (arXiv)

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.