nips nips2008 nips2008-16 nips2008-16-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jonathan L. Roux, Alain D. Cheveigné, Lucas C. Parra
Abstract: How does one extract unknown but stereotypical events that are linearly superimposed within a signal with variable latencies and variable amplitudes? One could think of using template matching or matching pursuit to find the arbitrarily shifted linear components. However, traditional matching approaches require that the templates be known a priori. To overcome this restriction we use instead semi Non-Negative Matrix Factorization (semiNMF) that we extend to allow for time shifts when matching the templates to the signal. The algorithm estimates templates directly from the data along with their non-negative amplitudes. The resulting method can be thought of as an adaptive template matching procedure. We demonstrate the procedure on the task of extracting spikes from single channel extracellular recordings. On these data the algorithm essentially performs spike detection and unsupervised spike clustering. Results on simulated data and extracellular recordings indicate that the method performs well for signalto-noise ratios of 6dB or higher and that spike templates are recovered accurately provided they are sufficiently different. 1
[1] S. Mallat and Z. Zhang, “Matching pursuit with time-frequency dictionnaries,” IEEE Trans. Signal Process., vol. 41, pp. 3397–3415, 1993.
[2] C. Ding, T. Li, and M. I. Jordan, “Convex and semi-nonnegative matrix factorization for clustering and low-dimension representation,” Lawrence Berkeley National Laboratory, Tech. Rep. LBNL-60428, 2006.
[3] T. Li and C. Ding, “The relationships among various nonnegative matrix factorization methods for clustering,” in Proc. ICDM, 2006, pp. 362–371.
[4] M. Mørup, M. N. Schmidt, and L. K. Hansen, “Shift invariant sparse coding of image and music data,” Technical University of Denmark, Tech. Rep. IMM2008-04659, 2008.
[5] H. Kameoka, N. Ono, K. Kashino, and S. Sagayama, “Complex NMF: A new sparse representation for acoustic signals,” in Proc. ICASSP, Apr. 2009.
[6] P. X. Joris, L. H. Carney, P. H. Smith, and T. C. T. Yin, “Enhancement of neural synchronization in the anteroventral cochlear nucleus. I. Responses to tones at the characteristic frequency,” J. Neurophysiol., vol. 71, pp. 1022–1036, 1994.
[7] S. Arkadiusz, M. Sayles, and I. M. Winter, “Spike waveforms in the anteroventral cochlear nucleus revisited,” in ARO midwinter meeting, no. Abstract #678, 2008.
[8] M. Mørup, K. H. Madsen, and L. K. Hansen, “Shifted non-negative matrix factorization,” in Proc. MLSP, 2007, pp. 139–144.
[9] C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning, ser. Adaptive Computation and Machine Learning. Cambridge, MA: The MIT Press, Jan. 2006.
[10] K. Fang, S. Kotz, and K. Ng, Symmetric Multivariate and Related Distributions. Chapman and Hall, 1990. London:
[11] M. Rangaswamy, D. Weiner, and A. Oeztuerk, “Non-Gaussian random vector identification using spherically invariant random processes,” IEEE Trans. Aerospace and Electronic Systems, vol. 29, no. 1, pp. 111–123, Jan. 1993.
[12] J. Benesty, J. Chen, and Y. Huang, Microphone Array Signal Processing. Springer-Verlag, 2008. Berlin, Germany: