方向性条件化的低延迟神经滤波用于助听器语音增强
Directivity-Conditioned Low-Latency Neural Filtering for Speech Enhancement in Hearing Aids
浏览论文内容
中文总结 AI 辅助
本文提出一种低延迟（10毫秒）的深度神经网络，利用特征级线性调制（FiLM）调整方向性模式，并设计保持跨通道频谱关系的损失函数，在助听器场景下实现与宽松延迟方法相当的语音增强效果。
中文摘要 AI 辅助
神经方向性滤波的最新进展在推理阶段调整方向性模式的方向和形状方面表现出卓越的结果。然而，在现有的推理期间调整方向性模式的方法中，重要的现实世界约束被忽视了。特别是对于听力设备，场景通常更加动态，麦克风位置随头部直径和助听器放置而变化，出现头影效应，并且适用严格的延迟约束。在这项工作中，我们提出了一种新颖的低延迟（10毫秒）深度神经网络（DNN），考虑了听力设备的上述要求。与最近的工作一样，我们使用特征级线性调制（FiLM）在测试期间引导方向性模式。为了保持期望的方向性模式，提出了一种损失函数，该函数维持跨通道的频谱关系。有趣的是，我们能够实现与具有宽松延迟约束的方法相似的结果。
英文摘要
Latest advances in neural directional filtering show exceptional results in adapting the direction and shape of directivity patterns during the inference phase. However, in the existing methods for adapting directivity patterns during inference, important real-world constraints have been disregarded. Particularly for hearing devices, scenarios are often much more dynamic, microphone positions vary with head diameter and hearing aid placement, head-shadow effects occur, and strict latency constraints apply. In this work, we propose a novel low-latency (10 ms) deep neural network (DNN) taking the above requirements of hearing devices into account. As in recent work, we use feature-wise linear modulation (FiLM) to steer the directivity patterns during testing. To preserve the desired directivity pattern, a loss function is proposed that maintains the spectral cross-channel relationships. Interestingly, we are able to achieve similar results to methods with relaxed latency constraints.
发表机构
- University of Hamburg（汉堡大学）
机构由 AI 辅助整理，请以论文原文为准。
