QC-GAN: A Parameter-Efficient Quaternion Conformer GAN for High-Fidelity Speech Enhancement

Researchers introduce QC-GAN, a parameter-efficient speech enhancement model combining Quaternion Conformer architecture with MetricGAN training. The framework achieves state-of-the-art speech quality scores while using less than half the parameters of comparable models, with a 35K-parameter variant demonstrating viable ultra-lightweight performance.

QC-GAN represents a meaningful advancement in efficient machine learning architecture design, addressing a critical challenge in deploying AI models across resource-constrained devices. The framework leverages quaternion mathematics—using Hamilton products to encode magnitude and phase information through structured weight sharing—enabling substantial parameter reduction without sacrificing performance quality. This approach achieves a PESQ score of 3.48 with only 0.89M parameters, with an extremely compact 35K-parameter variant reaching 3.23, demonstrating that fundamental rearchitecture can outweigh brute-force scaling.

The broader context reveals an industry-wide shift toward parameter efficiency following the resource constraints exposed by large language models. Speech enhancement traditionally required extensive models, making deployment in mobile applications, embedded systems, and edge devices economically unfeasible. QC-GAN's success validates that structured mathematical approaches—rather than simply stacking layers—can achieve superior efficiency gains.

For developers and device manufacturers, this has immediate practical implications. Ultra-compact speech enhancement enables real-time processing on smartphones, IoT devices, and battery-constrained hardware without cloud connectivity requirements. The generalization demonstrated on DNS-Challenge 3 suggests robustness across varied acoustic conditions, critical for production deployment.

The research signals that continued progress in AI efficiency will come from algorithmic innovation rather than hardware scaling alone. Future developments likely involve applying quaternion and similar structured-weight approaches to other domains, plus hybrid models that adaptively adjust parameter counts based on input complexity.

• →QC-GAN achieves PESQ 3.48 with 0.89M parameters, matching state-of-the-art models at <50% their size.
• →Quaternion Conformer architecture uses Hamilton products for structured weight sharing, enabling efficient magnitude-phase encoding.
• →A 35K-parameter variant reaches PESQ 3.23, proving ultra-lightweight speech enhancement is viable.
• →MetricGAN-based training optimizes perceptual quality rather than traditional loss functions, improving subjective audio quality.
• →Validation on DNS-Challenge 3 demonstrates generalization to real-world noisy conditions beyond training datasets.