auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks

Musical genre classification of audio signals

G. Tzanetakis and P. Cook · 2002

Earlier work this paper cites.

LIBLINEAR: A library for large linear classification

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin · 2008

Earlier work this paper cites.

Unsupervised learning of sparse features for scalable audio classification

M. Henaff, K. Jarrett, K. Kavukcuoglu, and Y. LeCun · 2011

Earlier work this paper cites.

Representation learning: A review and new perspectives

Y. Bengio, A. Courville, and P. Vincent · 2013

Earlier work this paper cites.

Sequence to sequence learning with neural networks

I. Sutskever, O. Vinyals, and Q. V. Le · 2014

Cited alongside, same era.

Environmental sound classification with convolutional neural networks

K. J. Piczak · 2015

Cited alongside, same era.

Is deception emotional? An emotion-driven predictive approach

S. Amiriparian, J. Pohjalainen, E. Marchi, S. Pugachevskiy, and B. Schuller · 2016

Cited alongside, same era.

SoundNet: Learning sound representations from unlabeled video

Y. Aytar, C. Vondrick, and A. Torralba · 2016

Cited alongside, same era.

ESC: Dataset for environmental sound classification

K. J. Piczak

Cited in the paper.

Sequence to sequence autoencoders for unsupervised representation learning from audio

S. Amiriparian, M. Freitag, N. Cummins, and B. Schuller · 2017

Closest in time.

DCASE 2017 challenge setup: Tasks, datasets and baseline system

A. Mesaros, T. Heittola, A. Diment, B. Elizalde, A. Shah, E. Vincent, B. Raj, and T. Virtanen · 2017

Closest in time.

openXBOW — Introducing the Passau open-source crossmodal bag-of-words toolkit

M. Schmitt and B. W. Schuller · 2017

Closest in time.