{'fraction_non_alphanumeric': 0.023791250959324637, 'fraction_numerical': 0.012279355333844973, 'mean_word_length': 4.900452488687783, '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}}
DNN based multi-level feature ensemble for acoustic scene classification

Various characteristics can be used to define an acoustic scene, such as long-term context information and short-term events. This makes it difficult to select input features and pre-processing methods suitable for acoustic scene classification. In this paper, we propose an ensemble model that exploits various input features in which the strength for classifying an acoustic scene varies: i-vectors are used for segment-level representations of long-term context, spectrograms are used for frame-level short-term events, and raw waveforms are used to extract features that could be missed by existing methods. For each feature, we used deep neural network based models to extract a representation from an input segment. A separated scoring phase was then exploited to extract class-wise scores on a scale of 0 to 1 that could be used as confidence measures. Scores were extracted using Gaussian models and support vector machines. We tested the validity of the proposed framework using task 1 of detection, and classification of acoustic scenes and events 2018 dataset. The proposed framework had an accuracy of 73.82% for the pre-defined fold-1 validation setup and 74.8% for the evaluation setup which is 7th in team ranking.