An Automatic Sound ClassifiCation Framework …
437
29. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
30. I. McLoughlin, H. Zhang, Z. Xie, Y. Song, W. Xiao, Robust sound event classification using
deep neural networks. IEEE/ACM Trans. Audio, Speech, Language Process. 23(3), 540–552
(2015). March
31. T. Nishiura, S. Nakamura, An evaluation of sound source identification with RWCP sound
scene database in real acoustic environments, in Proceedings. IEEE International Conference
on Multimedia and Expo, 2002, vol. 2, pp. 265–268
32. R.G. Leonard, G. Doddington, Tidigits Speech Corpus (Linguistic Data Consortium, Philadelphia, 1993)
33. N. Morgan, H. Bourlard, Continuous speech recognition using multilayer perceptrons with
hidden markov models, in 1990 IEEE International Conference on Acoustics, Speech and
Signal Processing (IEEE, New York, 1990), pp. 413–416
34. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional
neural networks. Adv. Neural Inf. Process. Syst. pp. 1097–1105 (2012)
35. A. Graves, A. Mohamed, G.E. Hinton, Speech recognition with deep recurrent neural networks,
in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE,
New York, 2013), pp. 6645–6649
36. S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735–1780
(1997)
37. A.M. Saxe, J.L. McClelland, S. Ganguli, Exact solutions to the nonlinear dynamics of learning
in deep linear neural networks. arXiv preprint arXiv:1312.6120 (2013)
38. D. Kingma, J. Ba, Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980
(2014)
39. A. Varga, H.J. Steeneken, Assessment for automatic speech recognition: Ii. noisex-92: A
database and an experiment to study the effect of additive noise on speech recognition systems.
Speech Commun. 12(3), 247–251 (1993)
40. R. Gütig, H. Sompolinsky, Time-warp-invariant neuronal processing. PLoS Biol. 7(7),
e1000141 (2009)
41. K. Greff, R.K. Srivastava, J. Koutnk, B.R. Steunebrink, J. Schmidhuber, LSTM: A search space
odyssey. IEEE Trans. Neural Networks Learn. Syst. 28(10), 2222–2232 (2017)
42. J. Anumula, D. Neil, T. Delbruck, S.C. Liu, Feature representations for neuromorphic audio
spike streams. Front. Neurosci 12, 23 (2018)
43. M. Abdollahi, S.-C. Liu, Speaker-independent isolated digit recognition using an AER silicon
cochlea, in 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS), Nov 2011, pp.
269–272
44. D. Neil, S.-C. Liu, Effective sensor fusion with event-based sensors and deep network architectures, in 2016 IEEE International Symposium on Circuits and Systems (ISCAS), May 2016,
pp. 2282–2285
45. A. Tavanaei, A. Maida, Bio-inspired multi-layer spiking neural network extracts discriminative
features from speech signals, in International Conference on Neural Information Processing
(Springer, Berlin, 2017), pp. 899–908
46. A. Tavanaei, A. Maida, A spiking network that learns to extract spike signatures from speech
signals. Neurocomputing 240, 191–199 (2017)
47. Y. Zhang, P. Li, Y. Jin, Y. Choe, A digital liquid state machine with biologically inspired
learning and its application to speech recognition. IEEE Trans. Neural Networks Learn. Syst.
26(11), 2635–2649 (2015)
48. M. Zhang, H. Qu, A. Belatreche, X. Xie, EMPD: An efficient membrane potential driven
supervised learning algorithm for spiking neurons. IEEE Trans. Cogn. Dev. Syst. 99, 1–1
(2017)
49. R. Gütig, Spiking neurons can discover predictive features by aggregate-label learning. Science
351(6277), 4113 (2016)
50. F. Ponulak, A. Kasi´ nski, Supervised learning in spiking neural networks with Re Su Me:
sequence learning, classification, and spike shifting. Neural Comput. 22(2), 467–510 (2010)
437
29. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
30. I. McLoughlin, H. Zhang, Z. Xie, Y. Song, W. Xiao, Robust sound event classification using
deep neural networks. IEEE/ACM Trans. Audio, Speech, Language Process. 23(3), 540–552
(2015). March
31. T. Nishiura, S. Nakamura, An evaluation of sound source identification with RWCP sound
scene database in real acoustic environments, in Proceedings. IEEE International Conference
on Multimedia and Expo, 2002, vol. 2, pp. 265–268
32. R.G. Leonard, G. Doddington, Tidigits Speech Corpus (Linguistic Data Consortium, Philadelphia, 1993)
33. N. Morgan, H. Bourlard, Continuous speech recognition using multilayer perceptrons with
hidden markov models, in 1990 IEEE International Conference on Acoustics, Speech and
Signal Processing (IEEE, New York, 1990), pp. 413–416
34. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional
neural networks. Adv. Neural Inf. Process. Syst. pp. 1097–1105 (2012)
35. A. Graves, A. Mohamed, G.E. Hinton, Speech recognition with deep recurrent neural networks,
in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE,
New York, 2013), pp. 6645–6649
36. S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735–1780
(1997)
37. A.M. Saxe, J.L. McClelland, S. Ganguli, Exact solutions to the nonlinear dynamics of learning
in deep linear neural networks. arXiv preprint arXiv:1312.6120 (2013)
38. D. Kingma, J. Ba, Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980
(2014)
39. A. Varga, H.J. Steeneken, Assessment for automatic speech recognition: Ii. noisex-92: A
database and an experiment to study the effect of additive noise on speech recognition systems.
Speech Commun. 12(3), 247–251 (1993)
40. R. Gütig, H. Sompolinsky, Time-warp-invariant neuronal processing. PLoS Biol. 7(7),
e1000141 (2009)
41. K. Greff, R.K. Srivastava, J. Koutnk, B.R. Steunebrink, J. Schmidhuber, LSTM: A search space
odyssey. IEEE Trans. Neural Networks Learn. Syst. 28(10), 2222–2232 (2017)
42. J. Anumula, D. Neil, T. Delbruck, S.C. Liu, Feature representations for neuromorphic audio
spike streams. Front. Neurosci 12, 23 (2018)
43. M. Abdollahi, S.-C. Liu, Speaker-independent isolated digit recognition using an AER silicon
cochlea, in 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS), Nov 2011, pp.
269–272
44. D. Neil, S.-C. Liu, Effective sensor fusion with event-based sensors and deep network architectures, in 2016 IEEE International Symposium on Circuits and Systems (ISCAS), May 2016,
pp. 2282–2285
45. A. Tavanaei, A. Maida, Bio-inspired multi-layer spiking neural network extracts discriminative
features from speech signals, in International Conference on Neural Information Processing
(Springer, Berlin, 2017), pp. 899–908
46. A. Tavanaei, A. Maida, A spiking network that learns to extract spike signatures from speech
signals. Neurocomputing 240, 191–199 (2017)
47. Y. Zhang, P. Li, Y. Jin, Y. Choe, A digital liquid state machine with biologically inspired
learning and its application to speech recognition. IEEE Trans. Neural Networks Learn. Syst.
26(11), 2635–2649 (2015)
48. M. Zhang, H. Qu, A. Belatreche, X. Xie, EMPD: An efficient membrane potential driven
supervised learning algorithm for spiking neurons. IEEE Trans. Cogn. Dev. Syst. 99, 1–1
(2017)
49. R. Gütig, Spiking neurons can discover predictive features by aggregate-label learning. Science
351(6277), 4113 (2016)
50. F. Ponulak, A. Kasi´ nski, Supervised learning in spiking neural networks with Re Su Me:
sequence learning, classification, and spike shifting. Neural Comput. 22(2), 467–510 (2010)