{'fraction_non_alphanumeric': 0.0311587147030185, 'fraction_numerical': 0.019474196689386564, 'mean_word_length': 4.8742857142857146, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Motion beat induction based on short-term principal component analysis

We propose a novel tool called short-term principal component analysis (ST-PCA) to analyze motion capture (MoCap) data, which records realistic movements in a high dimensional time series. Our ST-PCA is successfully applied to beat induction, which is an important perception of human motion especially in dances and is required by many applications such as music synchronization [Kim et al. 2003; Shiratori et al. 2006]. Following [Kim et al. 2003], motion beats are defined as the regular moments when the movement is changed significantly in direction or magnitude. Different from the previous approaches [Kim et al. 2003; Shiratori et al. 2006] that analyze MoCap data in each channel, we estimate the motion beats regarding MoCap data as a whole with the proposed ST-PCA, which performs PCA in a sliding window. Our experiments demonstrate that our method can estimate much more accurate beats in a wide range of motions including complicated dances.