Description: The birth of Dynamic Time Warping (DTW) has a certain history (Itakura, a Japanese scholar), and its purpose is relatively simple. It is a method of measuring the similarity of two different time series of different lengths. It is widely used, mainly in template matching, for example, in isolated word speech recognition (whether two segments of speech represent the same word), hand gesture recognition, data mining and information retrieval.
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DTW.py
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