{'fraction_non_alphanumeric': 0.02681992337164751, 'fraction_numerical': 0.0019157088122605363, 'mean_word_length': 5.371951219512195, '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}}
Group Multi-Scale convolutional Network for Monaural Speech Enhancement in Time-domain

Recent researches show that convolutional neural networks (CNN) can effectively enhance speech signal by mod-eling its long-term dependence in time-domain. However, the unscaled speech sequence length challenges the receptive field of the convolutional speech enhancement system. This paper proposes a plug-and-play bottleneck module named group multi-scale (GMS) module to alleviate the receptive field craving of convolutional neural networks. The GMS module adopts Group-Communication fashion, where each feature group can send messages to both adjacent groups and output features by convolutional encoding. In this way, the series group forms a sub temporal convolutional network (TCN) in a single residual block, bringing several times the receptive field of the standard bottleneck module. Experimental results on TIMIT datasets show that the proposed module achieves 1.2 dB SI -SNR gain in the TasNet framework compared with the baseline Con-TasNet.