Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolution

• URL: http://arxiv.org/abs/2409.09337v2
• Date: Tue, 17 Sep 2024 17:33:57 GMT
• Title: Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolution
• Authors: Yongjoon Lee, Chanwoo Kim,
• Abstract summary: Speech Super-Resolution (SSR) is a task of enhancing low-resolution speech signals by restoring missing high-frequency components. Conventional approaches typically reconstruct log-mel features, followed by a vocoder that generates high-resolution speech in the waveform domain. We propose a method, referred to as Wave-U-Mamba, that directly performs SSR in time domain.
• Score: 4.495657539150699
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech Super-Resolution (SSR) is a task of enhancing low-resolution speech signals by restoring missing high-frequency components. Conventional approaches typically reconstruct log-mel features, followed by a vocoder that generates high-resolution speech in the waveform domain. However, as log-mel features lack phase information, this can result in performance degradation during the reconstruction phase. Motivated by recent advances with Selective State Spaces Models (SSMs), we propose a method, referred to as Wave-U-Mamba that directly performs SSR in time domain. In our comparative study, including models such as WSRGlow, NU-Wave 2, and AudioSR, Wave-U-Mamba demonstrates superior performance, achieving the lowest Log-Spectral Distance (LSD) across various low-resolution sampling rates, ranging from 8 kHz to 24 kHz. Additionally, subjective human evaluations, scored using Mean Opinion Score (MOS) reveal that our method produces SSR with natural and human-like quality. Furthermore, Wave-U-Mamba achieves these results while generating high-resolution speech over nine times faster than baseline models on a single A100 GPU, with parameter sizes less than 2% of those in the baseline models.

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