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A robust sound event recognition framework under TV playing conditions

机译:电视播放条件下强大的声音事件识别框架

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In this paper, we address the problem of performing sound event recognition tasks in the presence of television playing in a home environment. Our proposed framework consist of two modules: (1) a novel regression-based noise cancellation (RNC), a preprocessing which utilises a addition reference microphone placed near the television to reduce the noise. RNC learns an empirical mapping instead of the convention adaptive methods to achieve better noise reduction. (2) An improved subband power distribution image feature (iSPD-IF) which build on our existing classification framework by enhancing the feature extraction. A comprehensive experiment is carried out on our recorded data, which demonstrates high classification accuracy under severe television noise.
机译:在本文中,我们解决了在家庭环境中播放电视的存在中执行声音事件识别任务的问题。我们所提出的框架由两个模块组成:(1)基于新的基于回归的噪声消除(RNC),该预处理利用靠近电视附近的加法参考麦克风来减少噪声。 RNC了解了经验映射而不是“公约”自适应方法,以实现更好的降噪。 (2)通过增强特征提取,改进的子带配电图像特征(ISPD-IF),其在我们现有的分类框架上构建。在我们的记录数据上进行了全面的实验,这在严重的电视噪声下表现出高分类准确性。

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