# Explainable Deep Learning Analysis for Raga Identification in Indian Art Music

arXiv: https://arxiv.org/abs/2406.02443 · HTML: https://mlsanity.com/paper/2406.02443/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers](https://mlsanity.com/paper/1604.00825/) (2016)
- [audioLIME: Listenable Explanations Using Source Separation](https://mlsanity.com/paper/2008.00582/) (2020)

## Built on by (most cited first, 6 of 6)

- [Recognizing Ornaments in Vocal Indian Art Music with Active Annotation](https://mlsanity.com/paper/2505.04419/) (2025)
- [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)
- [Fusing Memory and Attention: A study on LSTM, Transformer and Hybrid Architectures for Symbolic Music Generation](https://mlsanity.com/paper/2603.21282/) (2026)
- [TCPα: Margin-Controlled Confidence estimation for reliable Music Information Retrieval](https://mlsanity.com/paper/2608.20326/) (2026)

## Abstract (arXiv)

Raga identification is an important problem within the domain of Indian Art music, as Ragas are fundamental to its composition and performance, playing a crucial role in music retrieval, preservation, and education. Few studies that have explored this task employ approaches such as signal processing, Machine Learning (ML), and more recently, Deep Learning (DL) based methods. However, a key question remains unanswered in all 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 an ample supply of rich, labeled datasets, which drives these ML/DL-based methods. In this paper, firstly we curate a dataset comprising 191 hours of Hindustani Classical Music (HCM) recordings, annotate it for Raga and tonic labels, and train a CNN-LSTM model for the task of Automatic Raga Identification (ARI). We achieve a chunk-wise f1-measure of 0.89 for a subset of 12 Raga classes. Following this, we make one of the first attempts to employ model explainability techniques: SoundLIME and GradCAM++ for Raga identification, to evaluate whether the classifier's predictions align with human understanding of Ragas. We compare the generated explanations with human expert annotations and further analyze individual test examples to understand the role of regions highlighted by explanations in making correct or incorrect predictions made by the model. Our results demonstrate a significant alignment of the model's understanding with human understanding, and the thorough analysis validates the effectiveness of our approach.