Voice activity detection (VAD) is the task of detecting speech in an audio
stream, which is challenging due to numerous unseen noises and low
signal-to-noise ratios in real environments. Recently, neural network-based
VADs have alleviated the degradation of performance to some extent. However,
the majority of existing studies have employed excessively large models and
incorporated future context, while neglecting to evaluate the operational
efficiency and latency of the models. In this paper, we propose a lightweight
and real-time neural network called MagicNet, which utilizes casual and depth
separable 1-D convolutions and GRU. Without relying on future features as
input, our proposed model is compared with two state-of-the-art algorithms on
synthesized in-domain and out-domain test datasets. The evaluation results
demonstrate that MagicNet can achieve improved performance and robustness with
fewer parameter costs.

该研究提出了一种轻量级实时的神经网络模型 MagicNet，利用 1-D 卷积和 GRU，并且不依赖未来特征作为输入，在综合测试数据集上与两种最先进的算法进行比较，评估结果表明 MagicNet 能够在较少的参数成本下实现更好的性能和鲁棒性。