Raga Net: A Novel Deep Learning Framework for Indian Raga Recognition based on Deep Convolution Neural Network and Long Short-term Memory

Raga Net: A Novel Deep Learning Framework for Indian Raga Recognition based on Deep Convolution Neural Network and Long Short-term Memory
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© 2025 by IJETT Journal
Volume-73 Issue-2
Year of Publication : 2025
Author : Swati Pravin Aswale, Wani Patil, Shrikant Chavate
DOI : 10.14445/22315381/IJETT-V73I2P113

How to Cite?
Swati Pravin Aswale, Wani Patil, Shrikant Chavate, "Raga Net: A Novel Deep Learning Framework for Indian Raga Recognition based on Deep Convolution Neural Network and Long Short-term Memory," International Journal of Engineering Trends and Technology, vol. 73, no. 2, pp. 155-165, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I2P113

Abstract
Indian Raga classification is challenging because of the vast disparity in the music's swaras, pitch, melody, style and intonation. This paper presents the Indian Raga classification using a Deep Convolution Neural Network and Multiple Acoustic Features. The multiple acoustic features include particular spectral, temporal, and voice quality attributes of the musical voice that describe the uniqueness of different ragas. A Novel hybrid Archimedes Optimization Algorithm in light of Multi-Attribute Utility Theory (AoA-MAUT) is utilized to choose salient and distinctive features from Multi Acoustic Features. Further, it uses the novel RagaNet, which combines Parallel Deep Convolution Neural Network (PDCNN) to depict the spectral properties of musical features and Long Short-Term Memory (LSTM) for providing temporal and long-term dependencies of the musical features. The proposed AoA-MAUT-RagaNet-based Indian classical raga recognition results in a general accuracy of 91.71%, a precision of 0.93, a recall of 0.90, and an F1-score of 0.91, which is superior compared to the traditional state of arts.

Keywords
Raga identification, Archimedes Optimization Algorithm, Multi-Attribute utility theory, Deep Convolution Neural Network, Multiple acoustic features.

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