6 papers
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…
Missing Melodies: AI Music Generation and its "Nearly" Complete Omission of the Global South
Atharva Mehta, Shivam Chauhan, Monojit Choudhury
Recent advances in generative AI have sparked renewed interest and expanded possibilities for music generation. However, the performance and versatility of these systems across mus…
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…
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…
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana +66
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cul…
The Zeno's Paradox of `Low-Resource' Languages
Hellina Hailu Nigatu, Atnafu Lambebo Tonja, Benjamin Rosman +2
The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced. However, there is li…