论文标题
通过临时麦克风阵列的神经语音增强的沟通成本意识麦克风选择
Communication-Cost Aware Microphone Selection For Neural Speech Enhancement with Ad-hoc Microphone Arrays
论文作者
论文摘要
在本文中，我们提出了一种通过临时麦克风阵列共同学习麦克风选择机制和语音增强网络的方法。基于注意力的麦克风选择机制经过训练，可以通过惩罚术语来减少沟通成本，该罚款代表任务绩效/沟通成本权衡。在折衷的过程中，我们的方法可以从较低的SNR场景中的更多麦克风中明智地流式传输，而在较高的SNR场景中却少了麦克风。我们通过移动源评估了复杂的回声声学场景中的模型，并表明它与从固定数量的麦克风中流式传输的模型的性能相匹配，同时降低了通信成本。
In this paper, we present a method for jointly-learning a microphone selection mechanism and a speech enhancement network for multi-channel speech enhancement with an ad-hoc microphone array. The attention-based microphone selection mechanism is trained to reduce communication-costs through a penalty term which represents a task-performance/ communication-cost trade-off. While working within the trade-off, our method can intelligently stream from more microphones in lower SNR scenes and fewer microphones in higher SNR scenes. We evaluate the model in complex echoic acoustic scenes with moving sources and show that it matches the performance of models that stream from a fixed number of microphones while reducing communication costs.
