4 citations · 6 across the 8 of their papers we have counts for
4 papers · 1 filter
Who Gets Heard? Rethinking Fairness in AI for Music Systems
Atharva Mehta, Shivam Chauhan, Megha Sharma +5
In recent years, the music research community has examined risks of AI models for music, with generative AI models in particular, raised concerns about copyright, deepfakes, and tr…
Exploring Adapter Design Tradeoffs for Low Resource Music Generation
Atharva Mehta, Shivam Chauhan, Monojit Choudhury
Fine-tuning large-scale music generation models, such as MusicGen and Mustango, is a computationally expensive process, often requiring updates to billions of parameters and, there…
Llama-3-Nanda-10B-Chat: An Open Generative Large Language Model for Hindi
Monojit Choudhury, Shivam Chauhan, Rocktim Jyoti Das +27
Developing high-quality large language models (LLMs) for moderately resourced languages presents unique challenges in data availability, model adaptation, and evaluation. We introd…
Music for All: Representational Bias and Cross-Cultural Adaptability of Music Generation Models
Atharva Mehta, Shivam Chauhan, Amirbek Djanibekov +3
The advent of Music-Language Models has greatly enhanced the automatic music generation capability of AI systems, but they are also limited in their coverage of the musical genres…