LMPAN: A Lightweight Multi-Path Alignment Network for Joint Full-Duplex Acoustic Echo Cancellation and Noise Suppression
Abstract
We propose a lightweight multi-path alignment network (LMPAN) for on-device joint acoustic echo cancellation (AEC) and noise suppression (NS) in full-duplex spoken dialogue systems. To address hardware-induced distortions and dynamic acoustic conditions, we introduce three core innovations: (1) a multi-path alignment stage correcting temporal and energy mismatches across reference, linear AEC (LAEC) output, and microphone signals; (2) an attention-based mechanism that dynamically integrates enhanced LAEC and microphone features under varying acoustic scenarios; (3) a post-filtering module with a dynamic target generation strategy for downstream tasks (ASR, VAD). Furthermore, we adopt a two-stage training framework leveraging self-supervised learning representations to enhance perceptual quality. Experiments show that LMPAN, with only 480K parameters and 126 MACs, achieves performance comparable to the state-of-the-art lightweight model DeepVQE-S, while ensuring real-time inference capability.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
LMPAN introduces a lightweight multi-path alignment network with SSL-guided training to achieve robust joint AEC and NS on edge devices. The paper presents a well-engineered hybrid solution that addresses the critical challenge of hardware-induced misalignments in full-duplex systems. By combining explicit temporal/energy alignment with attention-based fusion and perceptual SSL losses, it achieves a favorable balance between model size, computational cost, and performance. While not theoretically groundbreaking, the systematic integration of these components and the rigorous evaluation on downstream tasks make it a valuable contribution to practical audio signal processing for on-device AI.
Methodology
The paper proposes LMPAN, a lightweight network for joint Acoustic Echo Cancellation (AEC) and Noise Suppression (NS). The core methodological contribution lies in the explicit handling of temporal and energy misalignments between the reference, microphone, and Linear AEC (LAEC) outputs via a multi-path alignment stage. This is followed by an attention-based fusion module and a dynamic target adaptation strategy. The use of Self-Supervised Learning (SSL) representations (WavLM) in a two-stage training framework is a notable technical choice, aiming to preserve perceptual quality and semantic fidelity. However, the individual components (LAEC hybrid, attention fusion, SSL loss) are incremental combinations of existing techniques rather than fundamentally new algorithmic breakthroughs. The "multi-path alignment" is a practical engineering solution to a known problem (hardware latency) but lacks deep theoretical novelty in the context of modern end-to-end learning.
Results & evaluation
The experimental setup is rigorous, utilizing standard benchmarks (AEC Challenge 2023) and a self-collected real-world dataset from smartphones. The inclusion of downstream task evaluation (ASR, VAD, TIR) is a strong point, as it demonstrates the practical utility of the enhanced audio. The results show that LMPAN achieves performance comparable to DeepVQE-S with significantly fewer parameters (480K vs 820K) and lower computational cost (126M vs 315M MACs). The ablation studies effectively isolate the contributions of the alignment module, fusion module, and SSL training. The trade-off analysis of the Dynamic Target Adaptation (DTA) parameters provides valuable insight into balancing objective metrics (ERLE, PESQ) with subjective/perceptual quality (WER, MOS).
Reproducibility
The paper provides sufficient implementation details, including STFT parameters, optimizer settings, and dataset augmentation strategies. The use of standard libraries (WavLM, Paraformer) aids reproducibility. However, the specific hyperparameters for the dynamic target generation and the exact architecture of the GTCRN-based refinement branches could be more explicitly defined. The self-collected dataset details are described but not publicly available, which limits independent verification on real-world hardware variations.
Limitations
The primary limitation is the incremental nature of the contributions. The method relies heavily on a pre-computed LAEC stage, which, while common in hybrid systems, contradicts the trend towards fully end-to-end neural AEC. The performance gains, while consistent, are modest in absolute terms compared to the massive leaps seen in generative audio models. Furthermore, the "lightweight" claim is relative; 480K parameters is small for speech models but not negligible for extremely constrained edge devices. The reliance on SSL features adds inference overhead (though frozen) and complexity to the training pipeline.
Broader impact
This work contributes to the deployment of robust full-duplex spoken dialogue systems on edge devices, which is critical for the widespread adoption of always-on AI assistants. By improving the robustness of AEC and NS under hardware-induced distortions, it enhances the user experience and the reliability of downstream AI tasks like ASR and VAD. The emphasis on lightweight models supports sustainable AI by reducing computational resources required for real-time audio processing. LMPAN introduces a lightweight multi-path alignment network with SSL-guided training to achieve robust joint AEC and NS on edge devices. The paper presents a well-engineered hybrid solution that addresses the critical challenge of hardware-induced misalignments in full-duplex systems. By combining explicit temporal/energy alignment with attention-based fusion and perceptual SSL losses, it achieves a favorable balance between model size, computational cost, and performance. While not theoretically groundbreaking, the systematic integration of these components and the rigorous evaluation on downstream tasks make it a valuable contribution to practical audio signal processing for on-device AI.
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