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We develop a probabilistic generative model for representing acoustic event structure at multiple scales via a two-stage hierarchy. The first stage consists of a spiking representation which encodes a sound with a sparse set of kernels at different frequencies positioned precisely in time. The coarse time and frequency statistical structure of the first-stage spikes is encoded by a second stage spiking representation, while fine-scale statistical regularities are encoded by recurrent interactions within the first-stage. When fitted to speech data, the model encodes acoustic features such as harmonic stacks, sweeps, and frequency modulations, that can be composed to represent complex acoustic events. The model is also able to synthesize sounds from the higher-level representation and provides significant improvement over wavelet thresholding techniques on a denoising task.
Author Information
yan karklin (Knewton)
chaitu Ekanadham (Knewton, Inc.)
Eero Simoncelli (FlatIron Institute / New York University)
Eero P. Simoncelli received the B.S. degree in Physics in 1984 from Harvard University, studied applied mathematics at Cambridge University for a year and a half, and then received the M.S. degree in 1988 and the Ph.D. degree in 1993, both in Electrical Engineering from the Massachusetts Institute of Technology. He was an Assistant Professor in the Computer and Information Science department at the University of Pennsylvania from 1993 until 1996. He moved to New York University in September of 1996, where he is currently a Professor in Neural Science, Mathematics, and Psychology. In August 2000, he became an Associate Investigator of the Howard Hughes Medical Institute, under their new program in Computational Biology. His research interests span a wide range of topics in the representation and analysis of visual images, in both machine and biological systems.
Related Events (a corresponding poster, oral, or spotlight)
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2012 Spotlight: Hierarchical spike coding of sound »
Wed Dec 5th 11:38 -- 11:42 PM Room Harveys Convention Center Floor, CC
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2020 Poster: Learning efficient task-dependent representations with synaptic plasticity »
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2019 Poster: Flexible information routing in neural populations through stochastic comodulation »
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2017 Poster: Eigen-Distortions of Hierarchical Representations »
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2017 Oral: Eigen-Distortions of Hierarchical Representations »
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2012 Poster: Efficient and direct estimation of a neural subunit model for sensory coding »
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2011 Poster: Efficient coding with a population of Linear-Nonlinear neurons »
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2011 Poster: A blind sparse deconvolution method for neural spike identification »
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2011 Spotlight: A blind sparse deconvolution method for neural spike identification »
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2010 Poster: Implicit encoding of prior probabilities in optimal neural populations »
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2008 Oral: Reducing statistical dependencies in natural signals using radial Gaussianization »
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2008 Poster: Reducing statistical dependencies in natural signals using radial Gaussianization »
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2008 Tutorial: Statistical Models of Visual Images »
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2007 Poster: A Bayesian Model of Conditioned Perception »
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