Monitoring of Domestic Activities Using Multiple Beamformers and Attention Mechanism
Monitoring of Domestic Activities Using Multiple Beamformers and Attention Mechanism
复制标题
DOI：
10.2299/jsp.25.239
复制
发表时间：
2021-11
期刊：
影响因子：
--
通讯作者：
Y. Kaneko;Takeshi Yamada;S. Makino
中科院分区：
文献类型：
--
作者：
Y. Kaneko;Takeshi Yamada;S. Makino
声场景分类是对家庭活动进行分类的重要技术之一。当将家庭活动视为声学场景时，与声学场景分类的一般任务不同，存在目标场景和干扰场景的声音可能混合的问题。为了解决这个问题，我们提出了一种使用多个波束形成器和注意力机制的分类方法。在所提出的方法中，针对不同的目标方向准备多个波束形成器，并且将它们的输出输入到分类器。然后，所提出的方法估计每个波束形成器输出的重要性，通过使用注意力机制。为了验证该方法的有效性，将目标场景和干扰场景的声音混合生成声学数据，并进行了分类实验。实验结果表明，该方法能显著提高图像的F值。
Acoustic scene classification is one of the important technologies for classifying domestic activities. When considering domestic activities as acoustic scenes, unlike the general task of acoustic scene classification, there is the problem that the sounds of the target scene and interference scene can become mixed. To deal with this problem, we propose a classification method using multiple beamformers and an attention mechanism. In the proposed method, multiple beamformers for different target directions are prepared and their outputs are input to a classifier. The proposed method then estimates the importance of each beamformer output by using an attention mechanism. To verify the effectiveness of the proposed method, we generated acoustic data by mixing the sounds of the target scene and the interference scene, and conducted a classification experiment. The experimental results confirmed that the F-score could be greatly improved by the proposed method.