Evaluation of Mamba Architecture Design Choices for Real-Time Speech Enhancement
Year: 2026
Type: article
Abstract: Thanks to advances in structured state-space models, which effectively model long sequences with linear computational complexity, Mamba has become a promising alternative to Transformer-based architectures for speech enhancement. However, despite the recent proposal of numerous Mamba-based speech enhancement models, the impact of key architectural design choices on the trade-off between enhancement quality and computational efficiency remains unclear. This paper investigates the computational efficiency of Mamba-based architectures for speech enhancement, focusing on scenarios with limited computational resources. A systematic experimental study was conducted to evaluate the influence of three key architectural factors: the number of Mamba blocks, the embedding dimension, and unidirectional versus bidirectional processing. The models are compared using objective speech enhancement metrics and computational efficiency measures, including model size and inference time. The results provide a comprehensive analysis of the trade-offs between model complexity and enhancement performance.
Source: Applied Sciences
DOI: 10.3390/app162211136
Institutions: Vilnius University, University of Silesia in Katowice
Language: English
Topic: Speech and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7225757419
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