Language Preservation through Popularization of Regional Indian Music: A Data-Driven Approach

Abstract

The increased global connectivity due to internet penetration has resulted in the dominance of international languages and the decline of regional languages. To help preserve these languages, this study identifies the non-native regions in India that offer the most promising opportunities for expanding the popularity of music for each of the 14 regional languages. It analyses the audio features of 41,000 songs from 14 Indian languages on Spotify, employing two approaches – clustering algorithms and Random Forest Regressors. An overlap between the regions identified by both methods for any given language is considered a robust result, providing greater confidence in the results. The results show that while geographical proximity is a significant factor in determining non-native market fits for the music of a given language, alignment in preferences of audio features driven by cultural similarities plays an integral role as well. The findings have important implications for music industry stakeholders, including artists, management, digital platforms, and governments, who can leverage this data to devise strategies to expand the reach of regional music. This includes designing targeted marketing strategies, fostering cross-regional artist collaborations, and optimizing content recommendation algorithms, among others.