Abstract
We show how the “Online Sparse Coding Neural Gas” algorithm can be applied to a more realistic model of the “Cocktail Party Problem”. We consider a setting where more sources than observations are given and additive noise is present. Furthermore, we make the model even more realistic, by allowing the mixing matrix to change slowly over time. We also process the data in an online pattern-by-pattern way where each observation is presented only once to the learning algorithm. The sources are estimated immediately from the observations. In order to evaluate the influence of the change rate of the time dependent mixing matrix and the signal-to-noise ratio on the reconstruction performance with respect to the underlying sources and the true mixing matrix, we use artificial data with known ground truth.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Haykin, S., Chen, Z.: The cocktail party problem. Neural Comput. 17(9), 1875–1902 (2005)
Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7(6), 1129–1159 (1995)
Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)
Hyvärinen, A., Cristescu, R., Oja, E.: A fast algorithm for estimating overcomplete ica bases for image windows. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 1999, vol. 2, pp. 894–899 (1999)
Lee, T.W., Lewicki, M., Girolami, M., Sejnowski, T.: Blind source separation of more sources than mixtures using overcomplete representations. IEEE Signal Processing Letters 6(4), 87–90 (1999)
Lewicki, M.S., Sejnowski, T.J.: Learning Overcomplete Representations. Neural Computation 12(2), 337–365 (2000)
Theis, F., Lang, E., Puntonet, C.: A geometric algorithm for overcomplete linear ica. Neurocomputing 56, 381–398 (2004)
Hyvärinen, A.: Gaussian moments for noisy independent component analysis. IEEE Signal Processing Letters 6(6), 145–147 (1999)
Labusch, K., Barth, E., Martinetz, T.: Learning Data Representations with Sparse Coding Neural Gas. In: Verleysen, M. (ed.) Proceedings of the 16th European Symposium on Artificial Neural Networks, pp. 233–238. D-Side Publishers (2008)
Labusch, K., Barth, E., Martinetz, T.: Sparse Coding Neural Gas: Learning of Overcomplete Data Representations. Neurocomputing 72(7-9), 1547–1555 (2009)
Labusch, K., Barth, E., Martinetz, T.: Sparse Coding Neural Gas for the Separation of Noisy Overcomplete Sources. In: Koutník, J., Kůrková, V., Neruda, R. (eds.) ICANN 2008, Part I. LNCS, vol. 5163, pp. 788–797. Springer, Heidelberg (2008)
Pati, Y., Rezaiifar, R., Krishnaprasad, P.: Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In: Proceedings of the 27 th Annual Asilomar Conference on Signals, Systems (November 1993)
Donoho, D.L., Elad, M., Temlyakov, V.N.: Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Transactions on Information Theory 52(1), 6–18 (2006)
Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing 20(1), 33–61 (1998)
Rebollo-Neira, L., Lowe, D.: Optimized orthogonal matching pursuit approach. IEEE Signal Processing Letters 9(4), 137–140 (2002)
Oja, E.: A simplified neuron model as a principal component analyzer. J. Math. Biol. 15, 267–273 (1982)
Martinetz, T., Schulten, K.: A “Neural-Gas Network” Learns Topologies. Artificial Neural Networks I, 397–402 (1991)
Martinetz, T., Berkovich, S., Schulten, K.: “Neural-gas” Network for Vector Quantization and its Application to Time-Series Prediction. IEEE-Transactions on Neural Networks 4(4), 558–569 (1993)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation. IEEE Transactions on Signal Processing 54(11), 4311–4322 (2006); see also IEEE Transactions on Acoustics, Speech, and Signal Processing
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Labusch, K., Barth, E., Martinetz, T. (2009). Approaching the Time Dependent Cocktail Party Problem with Online Sparse Coding Neural Gas. In: Príncipe, J.C., Miikkulainen, R. (eds) Advances in Self-Organizing Maps. WSOM 2009. Lecture Notes in Computer Science, vol 5629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02397-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-02397-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02396-5
Online ISBN: 978-3-642-02397-2
eBook Packages: Computer ScienceComputer Science (R0)