In this paper we present an approach to polyphonic sound event detection in
real life recordings based on bi-directional long short term memory (BLSTM)
recurrent neural networks (RNNs). A single multilabel BLSTM RNN is trained to
map acoustic features of a mixture signal consisting of sounds from multiple
classes, to binary activity indicators of each event class. Our method is
tested on a large database of real-life recordings, with 61 classes (e.g.
music, car, speech) from 10 different everyday contexts. The proposed method
outperforms previous approaches by a large margin, and the results are further
improved using data augmentation techniques. Overall, our system reports an
average F1-score of 65.5% on 1 second blocks and 64.7% on single frames, a
relative improvement over previous state-of-the-art approach of 6.8% and 15.1%
respectively.

本文提出了一种基于 BLSTM 循环神经网络的多音事件检测方法，该方法通过训练一个多标签 BLSTM RNN，将包括音乐、车辆、讲话等 61 类在内的多种声音的声学特征映射到每个事件类别的二进制活动指示符上，并且通过使用数据增强技术进一步提高了识别结果的准确性，系统在实测中平均 F1 分数分别为 65.5％和 64.7％。