基于分层递归神经网络的嵌入式设备轻量级在线降噪
Lightweight Online Noise Reduction on Embedded Devices using Hierarchical Recurrent Neural Networks
摘要：
基于深度学习的降噪算法已经证明了它们的成功，特别是对于非平稳噪声，这使得人们希望将它们用于助听器（HA）等嵌入式设备。然而，这在目前用最先进的方法是不可能的。它们要么需要大量的参数和计算能力，因此只有使用现代CPU才可行。或者它们不适合在线处理，这需要滤波器组和算法本身的低延迟等约束。在这项工作中，我们提出了一种基于掩码的降噪方法。使用分层递归神经网络，我们能够大幅减少每层神经元的数量，同时通过分层连接包含时间上下文。这使我们能够优化我们的模型，使参数和浮点运算（FLOPs）的数量最少，同时与以前的工作相比，保持降噪质量。我们最小的网络只包含5k个参数，这使得该算法适用于嵌入式设备。我们评估我们的模型EUROM和现实世界的噪声数据库的混合物，并报告客观的指标看不见的噪声。
英文摘要：
Deep-learning based noise reduction algorithms have proven their success especially for non-stationary noises, which makes it desirable to also use them for embedded devices like hearing aids (HAs). This, however, is currently not possible with state-of-the-art methods. They either require a lot of parameters and computational power and thus are only feasible using modern CPUs. Or they are not suitable for online processing, which requires constraints like low-latency by the filter bank and the algorithm itself. In this work, we propose a mask-based noise reduction approach. Using hierarchical recurrent neural networks, we are able to drastically reduce the number of neurons per layer while including temporal context via hierarchical connections. This allows us to optimize our model towards a minimum number of parameters and floating-point operations (FLOPs), while preserving noise reduction quality compared to previous work. Our smallest network contains only 5k parameters, which makes this algorithm applicable on embedded devices. We evaluate our model on a mixture of EUROM and a real-world noise database and report objective metrics on unseen noise.
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