Indian Classical Music Synthesis
Published in 9th ACM IKDD CoDS and 27th COMAD (CoDS COMAD 2022), 2021
Recommended citation: Gaurav Viramgami, Hitarth Gandhi, Hrushti Naik, Nipun Mahajan, Praveen Venkatesh, Shivam Sahni, and Mayank Singh. 2022. Indian Classical Music Synthesis. In 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD) (CODS-COMAD 2022). Association for Computing Machinery, New York, NY, USA, 322–323. https://doi.org/10.1145/3493700.3493762 https://dl.acm.org/doi/abs/10.1145/3493700.3493762
Several studies in the fields of Artificial Intelligence and Natural Language Processing have been conducted on Music Synthesis. However, due to the limited availability of structured datasets, the field of Indian Classical Music remains unexplored. Additionally, the considerable influence of western music in the past decade has adversely affected the market and demand for Indian Classical Music. In this work, we propose a model to generate music for Indian Classical Music, specifically Carnatic Music, by leveraging the structured nature of Indian Carnatic Music. We build a dataset of Classical Indian Music with paired lyrics and melody to map melody with notes to extract features. Generative Adversarial Networks (GANs) are proven to be very effective for music generation in several research works. We experiment with GANs and Auto Encoder (Variational AE and Conditional VAE) on classical lyrics. The curated dataset shall also be helpful for further research in the domain of Indian Classical Music.
Recommended citation: Gaurav Viramgami, Hitarth Gandhi, Hrushti Naik, Nipun Mahajan, Praveen Venkatesh, Shivam Sahni, and Mayank Singh. 2022. Indian Classical Music Synthesis. In 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD) (CODS-COMAD 2022). Association for Computing Machinery, New York, NY, USA, 322–323. https://doi.org/10.1145/3493700.3493762