论文标题
DeepFilternEt2：在嵌入式设备上进行实时的演讲，以进行全乐波音频
DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio
论文作者
论文摘要
基于深度学习的语音增强已经取得了巨大改进，最近也扩展到完整的乐队音频（48 kHz）。但是，许多方法具有相当高的计算复杂性，并且需要大量的时间缓冲区才能实时使用，例如由于暂时的卷积或注意力。两者都使这些方法在嵌入式设备上不可行。这项工作进一步扩展了DeepFilternet，这利用了语音的谐波结构，允许提高语音（SE）。培训过程，数据增强和网络结构的几个优化导致最先进的SE性能，同时在笔记本Core-I5 CPU上将实时因子降低到0.04。这使得适用于实时在嵌入式设备上运行的算法。可以根据开源许可获得DeepFilternet框架。
Deep learning-based speech enhancement has seen huge improvements and recently also expanded to full band audio (48 kHz). However, many approaches have a rather high computational complexity and require big temporal buffers for real time usage e.g. due to temporal convolutions or attention. Both make those approaches not feasible on embedded devices. This work further extends DeepFilterNet, which exploits harmonic structure of speech allowing for efficient speech enhancement (SE). Several optimizations in the training procedure, data augmentation, and network structure result in state-of-the-art SE performance while reducing the real-time factor to 0.04 on a notebook Core-i5 CPU. This makes the algorithm applicable to run on embedded devices in real-time. The DeepFilterNet framework can be obtained under an open source license.
