DeSGOA: double exponential smoothing gazelle optimization algorithm-based deep learning model for blind source separation

Abstract

Blind Source Separation (BSS) pertains to a scenario, wherein the sources, as the method used for mixing are not known; only the mixed signals are accessible for subsequent separation. In several applications, it is preferable to retrieve all sources from the mixed signal or, at least isolate a specific source. The research proposes a novel approach, named Double Exponential Smoothing Gazelle Optimization Algorithm-based Generative Adversarial Network (DeSGOA-based GAN), and designed for BSS. The proposed algorithm, DeSGOA combines the power of Double Exponential Smoothing (DES) with the efficiency of the Gazelle Optimization Algorithm (GOA) to achieve superior results in source separation tasks. The research aims to enhance the accuracy and performance of BSS processes using the presented approach. At first, the mixed input signals attained from the dataset are fed to pre-processing phase. This phase aspires to eradicate noise present in the signal via the application of PCA model. The final objective is to capture a important amount of data information in the reduced dataset. Following this, the BSS process is carried out by utilizing the GAN, which is trained through the innovative DeSGOA algorithm. Experimental outcomes illustrate the efficacy of DeSGOA-based GAN method in achieving high-quality source separation, underscoring its potential as a valuable tool in audio signal processing and related applications. Finally, the experimental evaluation illustrated that the presented strategy gained SDR of 35.05, SIR of 11.94, SAR of 8.247, and ISR of 13.02.