噪声环境下的多通道关键词唤醒
Multichannel Keyword Spotting for Noisy Conditions
AI总结：
针对噪声环境下BF和ANC可能损伤有用信号的问题，提出带注意力机制的多通道神经网络KWS，可自适应选择或融合通道，并在实验室与真实智能音箱数据集上验证其降噪、唤醒性能与计算效率。
摘要：
本文提出了一种在噪声环境中改进关键词唤醒（KWS）算法的方法。尽管波束形成（BF）和自适应噪声消除（ANC）技术在某些条件下具有鲁棒性，但它们可能会因失真或抑制有用信号而降低激活系统的性能。作者提出了一种神经网络架构，该架构使用多个输入通道和注意力机制，使网络能够确定最有用的通道或其组合。该算法在两个数据集上展示了改进的质量：一个来自受控条件下的实验室，另一个来自自然条件下的智能音箱。所提算法与若干基线方法在降噪指标质量、KWS指标以及计算资源方面进行了比较，并与现有解决方案相对照。
英文摘要：
This article presents a method for improving a keyword spotter (KWS) algorithm in noisy environments. Although beamforming (BF) and adaptive noise cancellation (ANC) techniques are robust in some conditions, they may degrade the performance of the activation system by distorting or suppressing useful signals. The authors propose a neural network architecture that uses several input channels and an attention mechanism that allows the network to determine the most useful channel or their combination. The improved quality of the algorithm was demonstrated on two datasets: from a laboratory with controlled conditions and from smart speakers in natural conditions. The proposed algorithm was compared against several baselines in terms of the quality of noise reduction metrics, KWS metrics, and computing resources in comparison with existing solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
