论文标题
使用机器学习来增强基于动态扭曲的动态信号分类
Using Machine Learning to Augment Dynamic Time Warping Based Signal Classification
论文作者
论文摘要
语音识别等现代应用程序取决于将信号与预录录制的信号进行分类的能力。但是，这种比较通常需要忽略由于信号噪声，时间偏移，信号幅度和其他外部因素而引起的差异。动态时间扭曲（DTW）算法通过找到信号和非线性翘曲一个信号之间的相应区域来量化这种相似性，通过拉伸和缩小它。不幸的是，搜索所有信号的“扭曲”以找到最佳的相应区域在计算上很昂贵。 FastDTW算法可提高性能，但仅考虑小信号扭曲来牺牲准确性。 我的目标是提高DTW的速度，同时保持高精度。我的关键见解是，在任何特定的应用域中，信号都显示出特定类型的变化。例如，针对两个不同人测量的加速度计信号会根据其步幅长度和体重而有所不同。我的系统称为机器学习DTW（MLDTW），使用机器学习来学习特定域中常见的扭曲类型。然后，它使用学习的模型来适当地限制对潜在扭曲的搜索来提高DTW性能。我的结果表明，与FastDTW相比，MLDTW至少快速速度，并且在四个不同的数据集中，MLDTW平均将错误降低了60％。这些改进将显着影响多种应用（例如健康监测），并实现对多变量，较高频率和更长信号记录的更可扩展的处理。
Modern applications such as voice recognition rely on the ability to compare signals to pre-recorded ones to classify them. However, this comparison typically needs to ignore differences due to signal noise, temporal offset, signal magnitude, and other external factors. The Dynamic Time Warping (DTW) algorithm quantifies this similarity by finding corresponding regions between the signals and non-linearly warping one signal by stretching and shrinking it. Unfortunately, searching through all "warps" of a signal to find the best corresponding regions is computationally expensive. The FastDTW algorithm improves performance, but sacrifices accuracy by only considering small signal warps. My goal is to improve the speed of DTW while maintaining high accuracy. My key insight is that in any particular application domain, signals exhibit specific types of variation. For example, the accelerometer signal measured for two different people would differ based on their stride length and weight. My system, called Machine Learning DTW (MLDTW), uses machine learning to learn the types of warps that are common in a particular domain. It then uses the learned model to improve DTW performance by limiting the search of potential warps appropriately. My results show that compared to FastDTW, MLDTW is at least as fast and reduces errors by 60% on average across four different data sets. These improvements will significantly impact a wide variety of applications (e.g. health monitoring) and enable more scalable processing of multivariate, higher frequency, and longer signal recordings.
