标题： 婴儿在嘈杂环境中使用蓝图可分离卷积和时频循环神经网络的哭声检测 显示英文标题

标题： Infant Cry Detection In Noisy Environment Using Blueprint Separable Convolutions and Time-Frequency Recurrent Neural Network

摘要： 婴儿哭声检测是婴儿护理系统的重要组成部分。 在本文中，我们提出了一种轻量且鲁棒的婴儿哭声检测方法。 该方法利用蓝图可分离卷积来降低计算复杂度，并使用时间-频率循环神经网络进行自适应去噪。 该方法的整体框架结构是一个多尺度卷积循环神经网络，通过高效的空域注意力机制和对比感知通道注意力模块进行增强，并从对数梅尔频谱图的输入特征中获取局部和全局信息。 采用多个公共数据集来创建一个多样且具有代表性的数据集，并使用环境损坏技术生成在现实场景中遇到的噪声样本。 结果表明，在各种信噪比条件下，我们的方法在准确率、F1分数和复杂度方面均超过了多种最先进的方法。 代码位于 https://github.com/fhfjsd1/ICD_MMSP。 显示英文摘要

摘要： Infant cry detection is a crucial component of baby care system. In this paper, we propose a lightweight and robust method for infant cry detection. The method leverages blueprint separable convolutions to reduce computational complexity, and a time-frequency recurrent neural network for adaptive denoising. The overall framework of the method is structured as a multi-scale convolutional recurrent neural network, which is enhanced by efficient spatial attention mechanism and contrast-aware channel attention module, and acquire local and global information from the input feature of log Mel-spectrogram. Multiple public datasets are adopted to create a diverse and representative dataset, and environmental corruption techniques are used to generate the noisy samples encountered in real-world scenarios. Results show that our method exceeds many state-of-the-art methods in accuracy, F1-score, and complexity under various signal-to-noise ratio conditions. The code is at https://github.com/fhfjsd1/ICD_MMSP.