Audatic, Berlin, Friedrichstr. 210, 10117, Berlin, Germany.
Department of Otorhinolaryngology, Head and Neck Surgery, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Benjamin Franklin, Berlin, Germany.
Sci Rep. 2023 Feb 15;13(1):2719. doi: 10.1038/s41598-023-29871-8.
Almost half a billion people world-wide suffer from disabling hearing loss. While hearing aids can partially compensate for this, a large proportion of users struggle to understand speech in situations with background noise. Here, we present a deep learning-based algorithm that selectively suppresses noise while maintaining speech signals. The algorithm restores speech intelligibility for hearing aid users to the level of control subjects with normal hearing. It consists of a deep network that is trained on a large custom database of noisy speech signals and is further optimized by a neural architecture search, using a novel deep learning-based metric for speech intelligibility. The network achieves state-of-the-art denoising on a range of human-graded assessments, generalizes across different noise categories and-in contrast to classic beamforming approaches-operates on a single microphone. The system runs in real time on a laptop, suggesting that large-scale deployment on hearing aid chips could be achieved within a few years. Deep learning-based denoising therefore holds the potential to improve the quality of life of millions of hearing impaired people soon.
全球有近 5 亿人患有听力障碍。虽然助听器可以部分补偿听力损失，但很大一部分使用者在有背景噪音的情况下难以理解言语。在这里，我们提出了一种基于深度学习的算法，可以选择性地抑制噪声，同时保持语音信号。该算法将助听器用户的言语可懂度恢复到正常听力的对照受试者的水平。它由一个深度网络组成，该网络在一个大型定制噪声语音信号数据库上进行训练，并通过一种新的基于深度学习的语音可懂度度量标准进行神经架构搜索进一步优化。该网络在一系列人工分级评估中实现了最先进的去噪效果，能够在不同的噪声类别中泛化，并且与经典的波束形成方法不同，它可以在单个麦克风上运行。该系统可以在笔记本电脑上实时运行，这表明在未来几年内，大规模地将其应用于助听器芯片上是可行的。基于深度学习的去噪因此有可能在不久的将来提高数以百万计听力受损人士的生活质量。
