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Lookup NU author(s): Pengming Feng
This is the final published version of a conference proceedings (inc. abstract) that has been published in its final definitive form by International Speech Communication Association, 2017.
For re-use rights please refer to the publisher's terms and conditions.
Copyright © 2017 ISCA. We study the problem of dictionary learning for signals that can be represented as polynomials or polynomial matrices, such as convolutive signals with time delays or acoustic impulse responses. Recently, we developed a method for polynomial dictionary learning based on the fact that a polynomial matrix can be expressed as a polynomial with matrix coefficients, where the coefficient of the polynomial at each time lag is a scalar matrix. However, a polynomial matrix can be also equally represented as a matrix with polynomial elements. In this paper, we develop an alternative method for learning a polynomial dictionary and a sparse representation method for polynomial signal reconstruction based on this model. The proposed methods can be used directly to operate on the polynomial matrix without having to access its coefficients matrices. We demonstrate the performance of the proposed method for acoustic impulse response modeling.
Author(s): Guan J, Wang X, Feng P, Dong J, Wang W
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Proceedings of the Annual Conference of the International Speech Communication Association (Interspeech 2017)
Year of Conference: 2017
Pages: 3068-3072
Online publication date: 24/08/2017
Acceptance date: 02/04/2016
Date deposited: 18/01/2018
ISSN: 1990-9772
Publisher: International Speech Communication Association
URL: https://doi.org/10.21437/Interspeech.2017-395
DOI: 10.21437/Interspeech.2017-395
