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

2026-06-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Speech Processing · Depth: Expert, quick

Summary

QC-GAN is a parameter-efficient speech enhancement framework that integrates a Quaternion Conformer generator with MetricGAN-based training. It utilizes the Hamilton product for encoding magnitude and phase through structured weight sharing, which effectively reduces layer parameters while maintaining their interdependencies. A metric-learning discriminator is employed to maximize perceptual quality by optimizing approximate perceptual evaluation scores. On the VoiceBank+DEMAND dataset, QC-GAN achieved a Perceptual Evaluation of Speech Quality (PESQ) score of 3.48 with only 0.89M parameters, delivering performance comparable to state-of-the-art models at less than half their size. A 35K-parameter variant also reached a PESQ score of 3.23, surpassing conventional methods with significantly fewer parameters, and its generalization was confirmed on the DNS-Challenge 3 dataset.

Key takeaway

For Machine Learning Engineers developing speech enhancement systems, QC-GAN presents a compelling option to achieve high perceptual quality with significantly reduced model sizes. You can deploy models like the 0.89M parameter variant for performance comparable to larger state-of-the-art systems, or the 35K parameter version for extreme efficiency. This allows you to optimize resource usage without compromising audio fidelity, especially for edge or resource-constrained applications.

Key insights

QC-GAN offers high-fidelity speech enhancement with significantly fewer parameters by combining Quaternion Conformers and MetricGAN training.

Principles

• Hamilton product enables parameter reduction.
• Structured weight sharing preserves interdependencies.
• Metric-learning discriminators optimize perceptual quality.

Method

Combine a Quaternion Conformer generator with MetricGAN-based training, using a Hamilton product for magnitude/phase encoding and a metric-learning discriminator for perceptual quality optimization.

In practice

• Deploy high-quality speech enhancement.
• Reduce model size for edge devices.
• Improve audio processing efficiency.

Topics

• Speech Enhancement
• QC-GAN
• Quaternion Conformer
• Generative Adversarial Networks
• Parameter Efficiency
• Metric Learning

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.