Mapping the Sound of India: Machine Learning Based Regional Classification of Folk Songs 
Vrushali K. Solanke1, Snehalata B. Shirude2
Vrushali K. Solanke1, School of Computer Science, Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon (Maharashtra), India.
Dr. Snehalata B. Shirude2, School of Computer Science, Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon (Maharashtra), India.
Manuscript received on 31 July 2025 | First Revised Manuscript received on 08 August 2025 | Second Revised Manuscript received on 16 November 2025 | Manuscript Accepted on 15 December 2025 | Manuscript published on 30 December 2025 | PP: 1-8 | Volume-15 Issue-2, December 2025 | Retrieval Number: 100.1/ijeat.F469114060825 | DOI: 10.35940/ijeat.F4691.15021225
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Preserving Indian folk music in digital repositories poses significant challenges because robust classification systems are lacking to capture its linguistic, instrumental, and acoustic diversity. As a cornerstone of India’s intangible cultural heritage, this music faces the risk of marginalisation and loss unless systematic, scalable methods are employed to identify and preserve it. This research aims to develop an automated, multi-modal framework for regional classification of Indian folk music, thereby enabling structured archiving and improved accessibility. To achieve this, a novel machine learning pipeline was designed, integrating Whisper for speech recognition and regional language identification [3]. Instrument detection was performed using YAMNet, which has proven effective in recognizing traditional instruments [14]. Acoustic features such as MFCCs, chroma, and spectral descriptors were extracted using Librosa [Error! R eference source not found.]. Together, these tools provide a comprehensive understanding of the songs’ linguistic, instrumental, and rhythmic content. The curated dataset includes folk music from linguistically rich regions of India, such as Marathi, Punjabi, Urdu Qawwali, and dialects from Uttar Pradesh and Bihar. Seven supervised learning algorithms were trained and evaluated, including Random Forest, Support Vector Machine, and Gradient Boosting. Simpler classifiers, such as K-Nearest Neighbours, Naive Bayes, and Logistic Regression, were also tested. A hybrid ensemble model combining Random Forest, SVM, and Gradient Boosting through soft voting achieved a classification accuracy of 99%. This result demonstrates the effectiveness of ensemble learning, combined with multimodal features, in handling nuanced differences in regional folk genres. This research addresses the critical gap in scalable and automated tools for preserving folk music. The study highlights the potential of artificial intelligence in safeguarding endangered cultural assets
Keywords: Acoustic Features, Audio Classification, Machine Learning, Regional Music, K-Nearest Neighbours (KNN), Support Vector Classifier (SVC).
Scope of the Article: Artificial Intelligence and Methods