The hearing loss of almost half a billion people is commonly treated with hearing aids. However, current hearing aids often do not work well in real-world noisy environments. We present a deep learning based denoising system that runs in real time on iPhone 7 and Samsung Galaxy S10 (25ms algorithmic latency). The denoised audio is streamed to the hearing aid, resulting in a total delay of around 75ms. In tests with hearing aid users having moderate to severe hearing loss, our denoising system improves audio across three tests: 1) listening for subjective audio ratings, 2) listening for objective speech intelligibility, and 3) live conversations in a noisy environment for subjective ratings. Subjective ratings increase by more than 40%, for both the listening test and the live conversation compared to a fitted hearing aid as a baseline. Speech reception thresholds, measuring speech understanding in noise, improve by 1.6 dB SRT. Ours is the first denoising system that is implemented on a mobile device, streamed directly to users' hearing aids using only a single channel as audio input while improving user satisfaction on all tested aspects, including speech intelligibility. This includes overall preference of the denoised and streamed signal over the hearing aid, thereby accepting the higher latency for the significant improvement in speech understanding.

翻译：全球近五亿人的听力损失通常通过助听器进行干预，但现有助听器在真实嘈杂环境中往往效果不佳。我们提出一种基于深度学习的降噪系统，可在iPhone 7和三星Galaxy S10上实时运行（算法延迟25毫秒），降噪后的音频流式传输至助听器，总延迟约75毫秒。针对中度至重度听力损失的助听器使用者的测试显示，该降噪系统在以下三项测试中均提升了音频质量：1）主观音频评分聆听测试；2）客观言语可懂度测试；3）嘈杂环境下的实时对话主观评分。与基准助听器相比，聆听测试与实时对话的主观评分提升超过40%，反映噪声中言语理解能力的言语接收阈值改善1.6 dB SRT。本研究首次实现基于移动设备的降噪系统，仅通过单声道音频输入直接流式传输至助听器，并在所有测试维度（包括言语可懂度）提升用户满意度。用户更倾向于选择经降噪处理的流式信号而非直接使用助听器，表明其愿意接受较高延迟以换取言语理解能力的显著提升。