Exploratory Study Of Human-AI Interaction For Hindustani Music
This is an incremental study for practicing musicians to improve model design in music AI.
The paper tackled the problem of human-AI interaction for Hindustani music by studying participants using a novel generative model, finding challenges such as lack of restrictions and incoherence in model output.
This paper presents a study of participants interacting with and using GaMaDHaNi, a novel hierarchical generative model for Hindustani vocal contours. To explore possible use cases in human-AI interaction, we conducted a user study with three participants, each engaging with the model through three predefined interaction modes. Although this study was conducted "in the wild"- with the model unadapted for the shift from the training data to real-world interaction - we use it as a pilot to better understand the expectations, reactions, and preferences of practicing musicians when engaging with such a model. We note their challenges as (1) the lack of restrictions in model output, and (2) the incoherence of model output. We situate these challenges in the context of Hindustani music and aim to suggest future directions for the model design to address these gaps.