用小麦克风阵列实现高阶定向收音，无需复杂模型。
Neural Directional Filtering: Far-Field Directivity Control With a Small Microphone Array
- 用深度网络从阵列信号直接估计复数掩码，实现定向过滤。
- 小规模网络即可逼近理想指向性，支持高阶模式。
- 适合资源受限场景，如移动设备或小型语音系统。
在语音通信中，获取特定指向性的音频信号至关重要。本文提出一种基于深度神经网络（DNN）的定向滤波方法，无需依赖显式信号模型。具体而言，该方法利用DNN从麦克风阵列信号中估计出单通道复数掩码，并将其应用于参考麦克风，生成具有期望指向性模式的输出信号。研究了训练数据集构成对推理时指向性表现的影响。实验表明，使用相对较小的DNN，该方法能较好逼近目标指向性。此外，仅用少量麦克风即可实现高阶指向性模式，这是传统线性和参数化滤波难以达成的挑战。
原文摘要 · Abstract (English)
Capturing audio signals with specific directivity patterns is essential in speech communication. This study presents a deep neural network (DNN)-based approach to directional filtering, alleviating the need for explicit signal models. More specifically, our proposed method uses a DNN to estimate a single-channel complex mask from the signals of a microphone array. This mask is then applied to a reference microphone to render a signal that exhibits a desired directivity pattern. We investigate the training dataset composition and its effect on the directivity realized by the DNN during inference. Using a relatively small DNN, the proposed method is found to approximate the desired directivity pattern closely. Additionally, it allows for the realization of higher-order directivity patterns using a small number of microphones, which is a difficult task for linear and parametric directional filtering.
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