DEEP LEARNING FOR PERFORMANCE ASSESSMENT IN DANCE AND MUSIC - Arora, Mithhil; Bandyopadhyay, Samrat; Sharma, Mona; Sobti, Sakshi; buddhae, Sanika Sahastra; Hingmire, Anil (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Dance and music performance quality has been a consistently debated aspect of performance that has largely been based on human judgment which is subject to bias and lack of consistency. The recent progress in artificial intelligence, and especially deep learning provides a strong alternative to objective, data-driven assessment. This paper hopes to investigate the application of deep learning models Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Gated Recurrent Units (GRUs) to evaluate the performance of artists in these two areas. The proposed framework uses a mixture of multimodal data including motion capture or audio data and visual data to extract and combine features which determine rhythm and expression, synchronization and technical accuracy. The methodology focuses on the strong training, validation and testing plans to provide the accuracy and generalization to all the performers and genres. One use of this research is in real time feedback systems to learn music and dance, in competitions, automated scoring, and intelligent tutoring systems that adjust to the level of performance of the learner. Moreover, the paper identifies the opportunities of cross-cultural dataset growth, methods of bias mitigation, and explainable AI processes to provide transparency in automated assessments. The outcomes of the experiments prove the effectiveness of deep learning models to capture subtle features of performance, which is better than the traditional and classical approaches to machine learning. This study is part of the emerging convergence of artificial intelligence and performing arts, which will open the door to more equitable, more knowledgeable, and more globally applicable evaluation mechanisms.