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@conference{6833100028ab47bf9b00cb24bfac66fe, author = "Giambattista Parascandolo and Heikki Huttunen and Tuomas Virtanen", abstract = "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.", booktitle = "2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)", doi = "10.1109/ICASSP.2016.7472917", isbn = "978-1-4799-9988-0", month = "3", pages = "6440--6444", title = "{R}ecurrent {N}eural {N}etworks for {P}olyphonic {S}ound {E}vent {D}etection in {R}eal {L}ife {R}ecordings", year = "2016", }