论文信息 - DEEP LEARNING FOR DCASE 2017 CHALLENGE

DEEP LEARNING FOR DCASE 2017 CHALLENGE

This paper reports our results on all tasks of DCASE challenge 2017 which are acoustic scene classification, detection of rare sound events, sound event detection in real life audio, and large-scale weakly supervised sound event detection for smart cars. Our proposed methods are developed based on two favorite neural networks which are convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Experiments show that our proposed methods outperform the baseline.

Toan H. Vu | An Dang | Jia-Ching Wang

[1] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[2] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[3] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.

[4] Gerhard Widmer,et al. CP-JKU SUBMISSIONS FOR DCASE-2016 : A HYBRID APPROACH USING BINAURAL I-VECTORS AND DEEP CONVOLUTIONAL NEURAL NETWORKS , 2016 .

[5] Heikki Huttunen,et al. Recurrent neural networks for polyphonic sound event detection in real life recordings , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6] Kyogu Lee,et al. CONVOLUTIONAL NEURAL NETWORK WITH MULTIPLE-WIDTH FREQUENCY-DELTA DATA AUGMENTATION FOR ACOUSTIC SCENE CLASSIFICATION , 2016 .

[7] Toan H. Vu,et al. ACOUSTIC SCENE AND EVENT RECOGNITION USING RECURRENT NEURAL NETWORKS , 2016 .

[8] Ankit Shah,et al. DCASE2017 Challenge Setup: Tasks, Datasets and Baseline System , 2017, DCASE.

[9] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10] Heikki Huttunen,et al. Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection , 2017, IEEE/ACM Transactions on Audio, Speech, and Language Processing.