GANS FOR MUSICAL STYLE TRANSFER AND LEARNING - Ali, Syed Fahar; Rai, Keerti; Parikh, Swapnil M.; Rathour, Abhinav; Karunakaran, Manivannan; Kulkarni, Nishant (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Generative Adversarial Networks (GANs) are considered to be disruptive models of computational creativity, especially in music style transfer and learning. This study examines how GAN architecture may be incorporated in translating pieces of music between different stylistic domains without compromising their time and harmonious integrity. The conventional approaches including Autoencoders, RNNs, and Variational Autoencoders (VAEs) have shown a low success rate in the fine-grained representations of music which has led to the adoption of GANs due to their better generative realism. The suggested model uses Conditional GANs and CycleGANs, which allows supervised and unpaired learning with various musical data. The data normalization and preprocessing is done using feature extraction methods that are Mel-frequency cepstral coefficient (MFCCs), chroma features, and spectral contrast. The architecture focuses on balanced loss optimization between the discriminator and the generator and makes sure that there is convergence stability and audio fidelity. The results of experimental analysis show significant enhancement of melody preservation, timbre adaptation, and rhythmic consistency of genres. Moreover, the paper describes the use in AI-assisted composition, intelligent sound design, and interactive music education systems. These results highlight the value of GANs as creative processes, as well as educational instruments, enabling real-time modification of the style and music specifically synthesized to the user. The study, with its developed methodology of learning musical style using GAN and cross-domain adaptation, adds to an area of investigation of machine learning, cognition of music and digital creativity, which is being recently reshaped.