8 citations · 9 across the 4 of their papers we have counts for
4 papers
Data Turnstile: A Scalable Open Framework for Function-Calling Data Generation
Goutham Ramakrishnan, Megha Sharma
Small language models (SLMs) are attractive for agentic deployment due to low latency, reduced cost, and on-device privacy, yet they struggle with tool-use tasks where training dat…
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…
M2M-Gen: A Multimodal Framework for Automated Background Music Generation in Japanese Manga Using Large Language Models
Megha Sharma, Muhammad Taimoor Haseeb, Gus Xia +1
This paper introduces M2M Gen, a multi modal framework for generating background music tailored to Japanese manga. The key challenges in this task are the lack of an available data…
Foundation Models for Music: A Survey
Yinghao Ma, Anders Øland, Anton Ragni +39
In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This com…