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Section for Cognitive Systems
DTU Compute

02453 Course Material Download


All documents are available in Adobe Acrobat PDF or zip'ed PostScript. Software are stored as zip files.
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Projects

Jan Larsen: Introduction to 02453 and project descriptions, DTU Compute, 2015

Articles

Project : Spectral Estimation

John W. Adams: ``A New Optimal Window,'' IEEE Trans. on Signal Processing, vol. 39, no. 8, pp. 1753-1769, 1991.
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.
E. Allamanche, J. Herre, O. Helmuth, B. Frba, T. Kasten and M. Cremer: "MPEG-7 Spectral Flatness Measure Content-Based Identification of Audio Material Using MPEG-7 Low Level Description"

Project : Robust Adaptive Filtering

  S. Douglas, ``A Family of Normalized LMS Algorithms,'' IEEE Signal Processing Letters, vol. 1, no. 3, pp. 49-51, 1994.
  D. Erdogmus and J.C. Principe: ``Convergence Analysis of the Information Potential Criterion in Adaline Training,'' in Proc. of NNSP2001, Falmouth, Massachusetts, IEEE Press, 2001.
  D. Erdogmus and J. Principe: ``Comparison of entropy and mean square error criteria in adaptive system training using higher order statistics,'' Proc. 2nd Intl. Workshop on Independent Component Analysis (ICA'00), Helsinki, Finland, 2000.
  P. Kidmose, ``Adaptive Filtering for Non-Gaussian Processes,'' Proceedings of ICASSP2000, pp. 424-427, 2000.
  J. Principe, D. Xu, Q. Zhao, and J. Fisher: ``Learning from examples with information theoretic criteria. Learning from examples with information theoretic criteria,'' VLSI Signal Proc. Systems: special issue on neural networks, 2000.

Project : Comparison of Adaptive Algorithms

  D.T.M. Slock and T. Kailath: ``Numerically Stable Fast Transversal Filters for Recursive Least Squares Adaptive Filtering,'' IEEE Trans. on Signal Processing, vol. 39, no. 1, Jan. 1991.

Project : Subband Echo Cancellation

  S. Sandeep Pradham and V. U. Reddy: ``A New Approach to Subband Adaptive Filtering,'' IEEE Trans, on Signal Processing, vol. 47, no. 3, pp. 655--664, 1999.

Project : Signal Extraction

  S. Boll, ``Suppression of Acoustic Noise in Speech Using Spectral Subtraction'', IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 27, no. 2, pp. 113-120, 1979.
  D. Wang: ''On Ideal Binary Mask As the Computational Goal of Auditory Scene Analysis,'' in In P. Divenyi (Ed.), Speech Separation by Humans and Machines, pp. 181-197, Kluwer Academic, Norwell MA, 2005.
  M. N. Schmidt, J. Larsen, F. Hsiao: "Wind Noise Reduction using Non-negative Sparse Coding," IEEE International Workshop on Machine Learning for Signal Processing, pp. 431-436, Informatics and Mathematical Modelling, Technical University of Denmark, DTU, 2007
  D. Lee and S. Seung: ''Algorithms for Non-negative Matrix Factorization,'' in Proceedings of NIPS 2000, pp. 556-562.
  E. Vincent, R. Gribonval, and C. Fvotte: ''Performance Measurement in Blind Audio Source Separation,'' IEEE Transactions on Signal Processing, vol. 14, no. 4 July 2006.
  Extended spectral subtraction and Steven's Power Law: see attached excerpt from Kristian Timm Andersen's Master thesis project: Wind Noise Reduction in Single Channel Speech Signals, 2008.
     Bryan Pellom: ''MATLAB software package Objective Speech Quality Assessment''
  ISP Group: ''NMF Toolbox''
  MATLAB function: Inverse Spectogram

Project : Music Features

  Anders Meng, Peter Ahrendt, Jan Larsen, Lars Kai Hansen: "Temporal Feature Integration for Music Genre Classification," in IEEE Transactions on Audio and Speech and Language Processing, Nov. 2006.
[HTML]   The Echo Nest

Software

Software for Project 4: Comparison of Adaptive Algorithms .
Software for Project : Subband Acoustical Echo Cancellation.

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