02455 Course Material Download

Project 1: Music genre classification using neural networks

	Lecture handout
	Sound resources
	NN classification toolbox

		Jan Larsen: ``Introduction to Artificial Neural Networks,'' IMM, 1999.
		Meng, A., Ahrendt, P., Larsen, J., Improving Music Genre Classification by Short-Time Feature Integration, IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
		Ahrendt, P., Meng, A., Larsen, J., ``Decision time horizon for music genre classification using short tim efeatures, EUSIPCO 2004, pp. 1293-1296, 2004.

Project 2: Adaptive Regularization and Bayes Hyperparameters in Neural Network Filters

Lecture handout

		Jan Larsen: ``Introduction to Artificial Neural Networks,'' IMM, 1999.
		J. Larsen, C. Svarer, L. Nonboe Andersen & L.K. Hansen: ``Adaptive Regularization in Neural Network Modeling,'' in G.B. Orr, K. Müller (eds.) Neural Networks: Tricks of the Trade, Lecture Notes in Computer Science 1524, Germany: Springer-Verlag, Chapter 5, pp. 113-132, 1998.
		S. Sigurdsson, J. Larsen, L. K. Hansen, ``On Comparison of Adaptive Regularization Methods, Proceedings of the IEEE Workshop on Neural Networks for Signal Processing,'' vol. 10, pp. 221-230, 2000
		NN toolbox
		UCI Machine Learning Repository
		Sunspot data
		Mackey-Glass data
		Santa Fe time series competition data description, Data
		Time Series Prediction Competition The CATS Benchmark , Data
		Time Series Prediction Competition : Avanced Black-Box Techniques for Nonlinear Modeling: Theory and Applications
		Eunite competition

Project 3: On-line Training of Neural Network Filters

Lecture handout

		Jan Larsen: ``Introduction to Artificial Neural Networks,'' IMM, 1999.
		Jan Larsen: ``Design of Neural Network Filters,'' Ph.D. Thesis IMM, Chapter 5, in particular pp. 139-150, 1993.
		Y. Iiguni and H. Sakai, ``A Real-Time Learning Algorithm for a Multilayered Neural Network Baesd on the Extended Kalman Filter,'' IEEE Trans. on Signal Processing, vol. 40, no. 4, pp. 959-966, 1992.
		S. Singhal and L. Wu, ``Training feed-forward networks with the extended Kalman algorithm,'' Proc. ICASSP'89, D7.9, pp. 1187-1190, 1989.
		S. Kollias and D. Anastassiou, ``An adaptive least squares algorithm for the efficient training of artificial neural networks,'' IEEE Trans. Circuits and Systems, vol. 36, no. 9, pp. 1092-1101, 1989.
		UCI Machine Learning Repository
		Sunspot data
		Mackey-Glass data
		Santa Fe time series competition data description, Data
		Time Series Prediction Competition The CATS Benchmark , Data
		Time Series Prediction Competition : Avanced Black-Box Techniques for Nonlinear Modeling: Theory and Applications
		Eunite competition

Project 4: Comparison of Perceptron Neural Networks, Relevance Vector Machines and Gaussian Processes

		Lecture handout
		Ole Winther's Gaussian Process material
		Carl Rasmussens and Zoubin Ghahramani's Gaussian Process lecture, 2001
		Carl Rasmussens's Gaussian Process tools
		Carl Rasmussens's Conjugate Gradient minimizer (Matlab)
		Hans Bruun Nielsenm's BFGS minimizer (Matlab)
		Carl Rasmussens's Gaussian Process website
		NN toolbox
		UCI Machine Learning Repository
		Sunspot data
		Mackey-Glass data
		Santa Fe time series competition data description, Data
		Time Series Prediction Competition The CATS Benchmark , Data
		Time Series Prediction Competition : Advanced Black-Box Techniques for Nonlinear Modeling: Theory and Applications
		Eunite competition

		Jan Larsen: ``Introduction to Artificial Neural Networks,'' IMM, 1999.
		Joaquin Quiñonero-Candela and Lars Kai Hansen: ``Bayesian Methods, Kernel Methods, Time series prediction,'' International Conference on Acoustics, Speech, and Signal Processing, pp. 985-988, 2002
		Michael E. Tipping, "Sparse bayesian learning and the relevance vector machine," Journal of Machine Learning Research, vol. 1, pp. 211-244, 2001.
		C. Williams and C. Rasmussen: ``Gaussian Processes for Regression,'' in D.S. Touretzky, M.C. Mozer and M.E. Hasselmo (eds.) Proc. Conf. Advances in Neural Information Processing Systems, NIPS 8, MIT Press, 1996.

Project 5: Bayesian Signal Detection: Linear Systems

	Matlab pitch detection code
	Matlab signal detection code
	Tetsuya Shimamura: "Weighted Autocorrelation for Pitch Extraction of Noisy Speech," IEEE Transactions on Speech and Audio Processing, Vol. 9, No. 7, pp. 727-730, October 2001.
	E. Scheirer and M. Slaney: "Construction and Evaluation of a Robust Multifeature Speech/Music Discriminator," Proceedings of ICASSP97, April 21-24, Munich, Germany, 1997, pp. 1331-1334.
	J. Herre, E. Allamanche and O. Hellmuth: "Robust Matching of Audio Signals Using Spectral Flatness Features," Proceedings of the 2001 IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics, IEEE Press, 2001, pp. 127-130.
	L.K. Hansen, F. Å. Nielsen, J. Larsen: "Exploring fMRI Data for Periodic Signal Components", Artificial Intelligence in Medicine, vol. 25, pp. 25-44, 2002
	J.J.K. Ó Ruanaidh and W.J. Fitzgerald: Numerial Bayesian Methods Applied to Signal Processing, Springer Verlag, New York, 1996.

Project 6: Bayesian Signal Detection: Theory Project

	Matlab pitch detection code
	Matlab signal detection code

	Tetsuya Shimamura: "Weighted Autocorrelation for Pitch Extraction of Noisy Speech," IEEE Transactions on Speech and Audio Processing, Vol. 9, No. 7, pp. 727-730, October 2001.
	E. Scheirer and M. Slaney: "Construction and Evaluation of a Robust Multifeature Speech/Music Discriminator," Proceedings of ICASSP97, April 21-24, Munich, Germany, 1997, pp. 1331-1334.
	J. Herre, E. Allamanche and O. Hellmuth: "Robust Matching of Audio Signals Using Spectral Flatness Features," Proceedings of the 2001 IEEE Workshop on the Applications of Signal Processing to Audio and Acoustics, IEEE Press, 2001, pp. 127-130.
	L.K. Hansen, F. Å. Nielsen, J. Larsen: "Exploring fMRI Data for Periodic Signal Components", Artificial Intelligence in Medicine, vol. 25, pp. 25-44, 2002
	J.J.K. Ó Ruanaidh and W.J. Fitzgerald: Numerial Bayesian Methods Applied to Signal Processing, Springer Verlag, New York, 1996.

Project 7: Bayesian GLM for analysis of fMRI data

	fMRI data
	L.K. Hansen, F. Å. Nielsen, J. Larsen: "Exploring fMRI Data for Periodic Signal Components", Artificial Intelligence in Medicine, vol. 25, pp. 25-44, 2002
	R. Turner, A. Howseman, G.E. Rees, O. Josephs, K. Friston: "Functional magnetic resonance imaging of the human brain: data acquisition and analysis." Exp Brain Res 123(1-2):5-12, 1998
	Rik Henson: "Analysis of fMRI Timeseries: Linear Time-Invariant Models, Event-related fMRI and Optimal Experimental Design", Wellcome Dept. of Imaging Neuroscience, book chapter.

Project 8: Comparing supervised and unsupervised methods for analysis of fMRI data

	fMRI data
	R. Turner, A. Howseman, G.E. Rees, O. Josephs, K. Friston: "Functional magnetic resonance imaging of the human brain: data acquisition and analysis." Exp Brain Res 123(1-2):5-12, 1998
	L.K. Hansen, F. Å. Nielsen, J. Larsen: "Exploring fMRI Data for Periodic Signal Components", Artificial Intelligence in Medicine, vol. 25, pp. 25-44, 2002
	M.J. McKeown, L.K. Hansen, T.J. Sejnowsk: "Independent component analysis of functional MRI: what is signal and what is noise?". Curr Opin Neurobiol 13(5):620-9, 2003
	M.J. McKeown, T-P Jung, S. Makeig et al.: "Spatially independent activity patterns in functional MRI data during the Stroop color-naming task". Proc Natl Acad Sci U S A, 95(3):803-810, 1998
	C. Goutte, L.K. Hansen, M.G. Liptrot, E. Rostrup: "Feature-Space Clustering for fMRI Meta-Analysis," Human Brain Mapping, 13:165-183, 2001.

Project 9: Bayesian Neural Networks for Segmentation of MRI Brain Scan data

	Lecture handout
	fMRI data
	NN classification toolbox

		Jan Larsen: ``Introduction to Artificial Neural Networks,'' IMM, 1999.
		J. Ashburner, K. Friston: Image Segmentation, The Wellcome Dept. of Imaging Neuroscience, Queen Square, London, Book chapter.
		Y. Zhang, M. Brady, and S. Smith. Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Trans. on Medical Imaging, 20(1):45-57, 2001.

For further information, please contact

• Professor, Ph.D. Lars Kai Hansen,
  e-mail: lkhansen@imm.dtu.dk,
  Phone, +45 45253889
• Associate Professor, Ph.D. Jan Larsen,
  e-mail: jl@imm.dtu.dk,
  Phone, +45 45253923