GTCRN: A Speech Enhancement Model Requiring Ultralow Computational Resources

作者：Xiaobin Rong, Tianchi Sun, Xu Zhang, Yuxiang Hu, Changbao Zhu, Jing Lü · 年份：2024 · DOI：10.1109/icassp48485.2024.10448310 · 被引用次数：39 · 研究领域：Speech and Audio Processing、Speech Recognition and Synthesis、Infant Health and Development

While modern deep learning-based models have significantly outperformed traditional methods in the area of speech enhancement, they often necessitate a lot of parameters and extensive computational power, making them impractical to be deployed on edge devices in real-world applications. In this paper, we introduce Grouped Temporal Convolutional Recurrent Network (GTCRN), which incorporates grouped strategies to efficiently simplify a competitive model, DPCRN. Additionally, it leverages subband feature extraction modules and temporal recurrent attention modules to enhance its performance. Remarkably, the resulting model demands ultralow computational resources, featuring only 23.7 K parameters and 39.6 MMACs per second. Experimental results show that our proposed model not only surpasses RNNoise, a typical lightweight model with similar computational burden, but also achieves competitive performance when compared to recent baseline models with significantly higher computational resources requirements.