论文标题
深度学习的实时取消噪音
Real-time noise cancellation with Deep Learning
论文作者
论文摘要
生物测量通常被大量的非平稳噪声污染，这些噪声需要有效的降噪技术。我们提出了一种新的实时深度学习算法，该算法会产生反对噪声的信号，从而发生破坏性干扰。作为概念的证明，我们通过使用定制，柔性，3D打印的复合电极来减少电脑图中的肌电图噪声来证明该算法的性能。通过这种设置，通过消除宽带的肌肉噪声来实现EEG信噪比的平均4DB和最大提高脑电图的信噪比。这个概念不仅有可能自适应地提高脑电图的信噪比，而且可以应用于各种生物，工业和消费者应用，例如工业传感或降噪耳机。
Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so that destructive interference occurs. As a proof of concept, we demonstrate the algorithm's performance by reducing electromyogram noise in electroencephalograms with the usage of a custom, flexible, 3D-printed, compound electrode. With this setup, an average of 4dB and a maximum of 10dB improvement of the signal-to-noise ratio of the EEG was achieved by removing wide band muscle noise. This concept has the potential to not only adaptively improve the signal-to-noise ratio of EEG but can be applied to a wide range of biological, industrial and consumer applications such as industrial sensing or noise cancelling headphones.
