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In recent years, neural network approaches have shown superior performance to conventional hand-made features in numerous application areas.
A. Krogh, J. Vedelsby et al. , “Neural network ensembles, cross validation, and active learning,” Advances in neural information processing systems , vol. 7, pp. 231–238, 1995
1995
Earlier work this paper cites.
B. Logan et al. , “Mel frequency cepstral coefficients for music modeling.” in ISMIR , 2000
2000
Earlier work this paper cites.
Y. Nesterov et al. , “Gradient methods for minimizing composite objective function,” UCL, Tech. Rep., 2007
2007
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of Machine Learning Research , vol. 9, no. 2579-2605, p. 85, 2008
2008
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th International Conference on Machine Learning (ICML-10) , 2010, pp. 807–814
2010
Earlier work this paper cites.
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in International conference on artificial intelligence and statistics , 2010, pp. 249–256
2010
Earlier work this paper cites.
L. J. Van der Maaten and E. O. Postma, “Texton-based analysis of paintings,” in SPIE Optical Engineering+ Applications . International Society for Optics and Photonics, 2010, pp. 77 980H–77 980H
2010
Earlier work this paper cites.
P. Hamel, S. Lemieux, Y. Bengio, and D. Eck, “Temporal pooling and multiscale learning for automatic annotation and ranking of music audio.” in ISMIR , 2011, pp. 729–734
2011
Earlier work this paper cites.
2011
Earlier work this paper cites.
E. J. Humphrey and J. P. Bello, “Rethinking automatic chord recognition with convolutional neural networks,” in Machine Learning and Applications (ICMLA), 2012 11th International Conference on , vol. 2. IEEE, 2012, pp. 357–362
2012
Earlier work this paper cites.
J. Nam, J. Herrera, M. Slaney, and J. O. Smith, “Learning sparse feature representations for music annotation and retrieval.” in ISMIR , 2012, pp. 565–570
2012
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.
J. Nam, Z. Hyung, and K. Lee, “Acoustic scene classification using sparse feature learning and selective max-pooling by event detection,” IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events , 2013
2013
Earlier work this paper cites.
T. Heittola, A. Mesaros, A. Eronen, and T. Virtanen, “Context-dependent sound event detection,” EURASIP Journal on Audio, Speech, and Music Processing , vol. 2013, no. 1, pp. 1–13, 2013
2013
Earlier work this paper cites.
O. Abdel-Hamid, L. Deng, and D. Yu, “Exploring convolutional neural network structures and optimization techniques for speech recognition.” in INTERSPEECH , 2013, pp. 3366–3370
2013
Earlier work this paper cites.
D. Giannoulis, D. Stowell, E. Benetos, M. Rossignol, M. Lagrange, and M. D. Plumbley, “A database and challenge for acoustic scene classification and event detection,” in Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European . IEEE, 2013, pp. 1–5
2013
Earlier work this paper cites.
W. Nogueira, G. Roma, and P. Herrera, “Sound scene identification based on mfcc, binaural features and a support vector machine classifier,” IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events , 2013
2013
Cited alongside, same era.
M. Chum, A. Habshush, A. Rahman, and C. Sang, “Ieee aasp scene classification challenge using hidden markov models and frame based classification,” IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events , 2013
2013
Cited alongside, same era.
J. T. Geiger, B. Schuller, and G. Rigoll, “Recognising acoustic scenes with large-scale audio feature extraction and svm,” IEEE AASP Challenge: Detection and Classification of Acoustic Scenes and Events , 2013
2013
Cited alongside, same era.
K. Patil and M. Elhilali, “Multiresolution auditory representations for scene classification,” IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events , 2013
2013
Cited alongside, same era.
2014
Later among the works it cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Computer vision–ECCV 2014 . Springer, 2014, pp. 818–833
2014
Later among the works it cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Later among the works it cites.
D. Barchiesi, D. Giannoulis, D. Stowell, and M. D. Plumbley, “Acoustic scene classification: Classifying environments from the sounds they produce,” Signal Processing Magazine, IEEE , vol. 32, no. 3, pp. 16–34, 2015
2015
Later among the works it cites.
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B. Elizalde, H. Lei, G. Friedland, and N. Peters, “An i-vector based approach for audio scene detection,” IEEE AASP Challenge: Detection and Classification of Acoustic Scenes and Events , 2013
2013
Cited alongside, same era.
D. Li, J. Tam, and D. Toub, “Auditory scene classification using machine learning techniques,” IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events , 2013
2013
Cited alongside, same era.
D. Giannoulis, E. Benetos, D. Stowell, M. Rossignol, M. Lagrange, and M. D. Plumbley, “Detection and classification of acoustic scenes and events: An ieee aasp challenge,” in Applications of Signal Processing to Audio and Acoustics (WASPAA), 2013 IEEE Workshop on . IEEE, 2013, pp. 1–4
2013
Cited alongside, same era.
A. Rakotomamonjy and G. Gasso, “Histogram of gradients of time-frequency representations for audio scene classification,” IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events , 2013
2013
Cited alongside, same era.
G. Roma, W. Nogueira, P. Herrera, and R. de Boronat, “Recurrence quantification analysis features for auditory scene classification,” IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events , 2013
2013
Cited alongside, same era.
M. Lin, Q. Chen, and S. Yan, “Network in network,” arXiv preprint arXiv:1312.4400 , 2013
2013
Cited alongside, same era.
2013
Cited alongside, same era.
A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in Proc. ICML , vol. 30, 2013, p. 1
2013
Cited alongside, same era.
J. Hauswald, M. A. Laurenzano, Y. Zhang, C. Li, A. Rovinski, A. Khurana, R. G. Dreslinski, T. Mudge, V. Petrucci, L. Tang et al. , “Sirius: An open end-to-end voice and vision personal assistant and its implications for future warehouse scale computers,” in Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems . ACM, 2015, pp. 223–238
2015
Later among the works it cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Later among the works it cites.
T. Grill and J. Schlüter, “Music boundary detection using neural networks on combined features and two-level annotations,” in Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), Malaga, Spain , 2015
2015
Later among the works it cites.
T. N. Sainath, B. Kingsbury, G. Saon, H. Soltau, A.-r. Mohamed, G. Dahl, and B. Ramabhadran, “Deep convolutional neural networks for large-scale speech tasks,” Neural Networks , vol. 64, pp. 39–48, 2015
2015
Later among the works it cites.
2015
Later among the works it cites.
2015
Later among the works it cites.
2015
Later among the works it cites.
Y. Han, S. Lee, J. Nam, and K. Lee, “Sparse feature learning for instrument identification: Effects of sampling and pooling methods,” The Journal of the Acoustical Society of America , vol. 139, no. 5, pp. 2290–2298, 2016
2016
Closest in time.
Y. Han and K. Lee, “Detecting fingering of overblown flute sound using sparse feature learning,” EURASIP Journal on Audio, Speech, and Music Processing , vol. 2016, no. 1, pp. 1–10, 2016
2016
Closest in time.
Y. Han, J. Kim, and K. Lee, “Deep convolutional neural networks for predominant instrument recognition in polyphonic music,” ArXiv e-prints , May 2016
2016
Closest in time.
A. Mesaros, T. Heittola, and T. Virtanen, “TUT database for acoustic scene classification and sound event detection,” in 24th European Signal Processing Conference 2016 (EUSIPCO 2016) , Budapest, Hungary, 2016
2016
Closest in time.