{'fraction_non_alphanumeric': 0.008547008547008548, 'fraction_numerical': 0.0, 'mean_word_length': 5.605633802816901, '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}}
Multiple Viewpoint Systems for Music Classification

Abstract This paper describes a new statistical modelling method for music classification. The method is an extension of the multiple viewpoint method for music prediction and generation. A multiple viewpoint system significantly outperforms all component viewpoints on the tasks of folk tune genre and region classification. The method is successfully applied to predict the genres of unlabelled Basque folk tunes.