SwishNet: A Fast Convolutional Neural Network for Speech, Music and Noise Classification and Segmentation

Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean-square error log-spectral amplitude estimator,” IEEE transactions on acoustics, speech, and signal processing , vol. 33, no. 2, pp. 443–445, 1985

1985

Earlier work this paper cites.

Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” The handbook of brain theory and neural networks , vol. 3361, no. 10, p. 1995, 1995

1995

Earlier work this paper cites.

B. Logan, “Mel Frequency Cepstral Coefficients for Music Modeling,” in ISMIR , vol. 270, 2000, pp. 1–11

2000

Earlier work this paper cites.

J. Wolfe, “Speech and music, acoustics and coding, and what music might be ‘for’,” in Proc. 7th International Conference on Music Perception and Cognition , 2002, pp. 10–13

2002

Earlier work this paper cites.

G. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” IEEE Transactions on speech and audio processing , vol. 10, no. 5, pp. 293–302, 2002

2002

Earlier work this paper cites.

Y. Bengio, “Learning deep architectures for AI,” Foundations and trends® in Machine Learning , vol. 2, no. 1, pp. 1–127, 2009

2009

Earlier work this paper cites.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on . IEEE, 2009, pp. 248–255

2009

Earlier work this paper cites.

J. Kola, C. Espy-Wilson, and T. Pruthi, “Voice activity detection,” Merit Bien , pp. 1–6, 2011

2011

Earlier work this paper cites.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105

2012

Earlier work this paper cites.

M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Learning and transferring mid-level image representations using convolutional neural networks,” in Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on . IEEE, 2014, pp. 1717–1724

2014

Cited alongside, same era.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014

Original

2014

Cited alongside, same era.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

Original

2014

Cited alongside, same era.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, p. 436, 2015

2015

Cited alongside, same era.

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” arXiv preprint , 2016

2016

Later among the works it cites.

T. Sercu, C. Puhrsch, B. Kingsbury, and Y. LeCun, “Very deep multilingual convolutional neural networks for LVCSR,” in Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on . IEEE, 2016, pp. 4955–4959

2016

Later among the works it cites.

N. Takahashi, M. Gygli, B. Pfister, and L. Van Gool, “Deep convolutional neural networks and data augmentation for acoustic event detection,” arXiv preprint arXiv:1604.07160 , 2016

Original

2016

Later among the works it cites.

I. Loshchilov and F. Hutter, “SGDR: stochastic gradient descent with restarts,” arXiv preprint arXiv:1608.03983 , 2016

Original

2016

Later among the works it cites.