scholarly journals MTF-CRNN: Multiscale Time-Frequency Convolutional Recurrent Neural Network for Sound Event Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 147337-147348
Author(s):  
Keming Zhang ◽  
Yuanwen Cai ◽  
Yuan Ren ◽  
Ruida Ye ◽  
Liang He
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Event Detection ◽  
Time Frequency ◽  
Sound Event ◽  
Sound Event Detection

scholarly journals Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for \\Weakly Labeled Semi-supervised Sound Event Detection

2021 ◽  
Author(s):  
Janek Ebbers ◽  
Reinhold Haeb-Umbach
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Network ◽  
Event Detection ◽  
Forward Direction ◽  
Time Step ◽  
Sound Event ◽  
Sound Event Detection ◽  
Validation Set ◽  
Classi Fication

In this paper we present our system for thedetection and classi-fication of acoustic scenes and events (DCASE) 2020 ChallengeTask 4: Sound event detection and separation in domestic envi-ronments. We introduce two new models: the forward-backwardconvolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional neural network (CNN). The FBCRNNemploys two recurrent neural network (RNN) classifiers sharing thesame CNN for preprocessing. With one RNN processing a record-ing in forward direction and the other in backward direction, thetwo networks are trained to jointly predict audio tags, i.e., weak la-bels, at each time step within a recording, given that at each timestep they have jointly processed the whole recording. The pro-posed training encourages the classifiers to tag events as soon aspossible. Therefore, after training, the networks can be appliedto shorter audio segments of, e.g.,200 ms, allowing sound eventdetection (SED). Further, we propose a tag-conditioned CNN tocomplement SED. It is trained to predict strong labels while using(predicted) tags, i.e., weak labels, as additional input. For train-ing pseudo strong labels from a FBCRNN ensemble are used. Thepresented system scored the fourth and third place in the systemsand teams rankings, respectively. Subsequent improvements allowour system to even outperform the challenge baseline and winnersystems in average by, respectively,18.0 %and2.2 %event-basedF1-score on the validation set. Source code is publicly available athttps://github.com/fgnt/pb_sed

scholarly journals Multi-scale Convolutional Recurrent Neural Network and Data Augmentation for Polyphonic Sound Event Detection

2021 ◽  
Vol 1769 (1) ◽  
pp. 012008
Author(s):  
Keming Zhang ◽  
Yuanwen Cai ◽  
Yuan Ren ◽  
Ruida Ye ◽  
Xianwei Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Event Detection ◽  
Data Augmentation ◽  
Multi Scale ◽  
Sound Event ◽  
Sound Event Detection

Polyphonic Sound Event Detection and Classification using Convolutional Recurrent Neural Network with Mean Teacher

2021 ◽  
Author(s):  
Uday Singh ◽  
Dibya Debayan Dash ◽  
Manas Sharma ◽  
Sarthak Mishra ◽  
S. Malarvizhi ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Event Detection ◽  
Sound Event ◽  
Sound Event Detection
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