Adaptive attention transformer mechanism with CED model for end to end real-time single channel speech enhancement

Abstract Speech enhancement (SE) plays a vital role in applications such as mobile communication and automatic speech recognition. However, existing attention-based SE models often fail to simultaneously capture channel, frequency, and temporal dependencies, limiting their ability to generalize under diverse noise conditions. To address this, we propose AATCEDNet, a novel Adaptive Attention Transformer integrated with a Convolutional Encoder—Decoder (CED) framework for real-time single-channel speech enhancement. Our design introduces Adaptive Attention Transformer Network (AATN), composed of Adaptive Cross-Channel Attention (ACA) module for dynamic channel refinement, and dual-domain transformers combining Adaptive Frequency Attention (AFA) and Adaptive Time Attention (ATA) to effectively model long-range time-frequency dependencies. Furthermore, a dense encoder—decoder with D2Net blocks captures multi-scale features, while a subpixel convolution decoder with dual-path masking ensures accurate spectrogram reconstruction at low computational cost. Evaluations on OpenSLR (Telugu), NOIZEUS, and VoiceBank-DEMAND datasets demonstrate that AATCEDNet consistently outperforms state-of-the-art baselines such as MSCUNet, SCU-Net, and SEGAN. Under unseen noise conditions, AATCEDNet achieves average improvements of +1.7 % STOI and +0.33 PESQ compared to MSCUNet, while reducing model complexity. Ablation studies further confirm the critical roles of ACA and ATA in improving intelligibility. These results highlight AATCEDNet’s ability to deliver real-time, high-quality speech enhancement with strong generalization across diverse noise environments, making it suitable for practical real-time speech-driven systems.