Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolution
<|reference_start|>Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolution: 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.<|reference_end|>
@article{lee2024wave-u-mamba:,
    title={Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient
  Speech Super Resolution},
    author={Yongjoon Lee, Chanwoo Kim},
    journal={arXiv preprint arXiv:2409.09337},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09337},
    primaryClass={eess.AS cs.AI cs.SD}
}