28.08.2026journal-article
Engineering Research Express
A deep residual complex learning framework with long-range temporal context for phase-aware speech enhancement
Abstract In this paper, we propose an advanced speech enhancement model capable of effectively separating clean speech from noisy audio signals. The primary objective here is to improve speech intelligibility and quality in noisy environments while preserving critical speech components. We propose a GAN based novel residual learning architecture with long range temporal context using complex spectrograms. This is achieved by combining residual neural networks with dilated temporal processing. The model processes bo …