{'fraction_non_alphanumeric': 0.018252933507170794, 'fraction_numerical': 0.0, 'mean_word_length': 5.095238095238095, '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}}
Bird sound recognition based on novel classifier

With the rapid development of the Internet, voice recognition has become one of the core technologies on information era. Bird monitoring through sound recognition can be used as an effective indicator of wetland environmental quality. In this paper, we use Python to classify birds based on the features of Mel frequency cepstrum coefficient via K-Nearest Neighbor, support vector machine and multi-layer perceptron. Further, we carry out the comparisons of these algorithms and propose a novel classifier on the base of them. The experimental results show that the new classifier absorbs the fast prediction speed of the Multi-Layer Perception, the high accuracy and strong noise immunity of the K-Nearest Neighbor.