Evaluation of SSIM loss function in RIR generator GANs

Abstract

This study explores the potential of integrating the structural similarity (SSIM) as a loss function within generative adversarial networks (GANs) to enhance the generation of room impulse responses (RIRs). Neural network-based RIR generators sometimes introduce glitches into the generated RIRs, leading to distortion in the reconstructed signals. In this study, GANs are trained using three different loss functions: Mean Squared Error (MSE), SSIM and a combination of SSIM and MSE. Incorporating SSIM within the loss function improves the quality of generated RIRs and reduces glitches. Empirical findings highlight the effectiveness of GANs trained with the mixed MSE and SSIM loss function, resulting in RIR signals with fewer glitches, lower MSE values, and higher SSIM values. To evaluate the broader applicability of this approach and contribute to available resources, we introduce a novel RIR dataset named GTU-RIR. This dataset is presented alongside existing datasets such as BUT ReverbDB, enabling a comprehensive evaluation of the proposed method.