Alternating minimisation for glottal inverse filtering

Show simple item record Bleyer, Ismael Rodrigo Lybeck, Lasse Auvinen, Harri Airaksinen, Manu Alku, Paavo Siltanen, Samuli 2017-06-27T10:52:01Z 2017-06-27T10:52:01Z 2017-06
dc.identifier.citation Bleyer , I R , Lybeck , L , Auvinen , H , Airaksinen , M , Alku , P & Siltanen , S 2017 , ' Alternating minimisation for glottal inverse filtering ' , Inverse Problems , vol. 33 , no. 6 , 065005 .
dc.identifier.other PURE: 86114102
dc.identifier.other PURE UUID: 0cc61477-6615-4c5a-b1a7-47ce88d8d353
dc.identifier.other WOS: 000402405000001
dc.identifier.other Scopus: 85020039048
dc.identifier.other ORCID: /0000-0002-5988-5232/work/86938482
dc.description.abstract A new method is proposed for solving the glottal inverse filtering (GIF) problem. The goal of GIF is to separate an acoustical speech signal into two parts: the glottal airflow excitation and the vocal tract filter. To recover such information one has to deal with a blind deconvolution problem. This ill-posed inverse problem is solved under a deterministic setting, considering unknowns on both sides of the underlying operator equation. A stable reconstruction is obtained using a double regularization strategy, alternating between fixing either the glottal source signal or the vocal tract filter. This enables not only splitting the nonlinear and nonconvex problem into two linear and convex problems, but also allows the use of the best parameters and constraints to recover each variable at a time. This new technique, called alternating minimization glottal inverse filtering (AM-GIF), is compared with two other approaches: Markov chain Monte Carlo glottal inverse filtering (MCMC-GIF), and iterative adaptive inverse filtering (IAIF), using synthetic speech signals. The recent MCMC-GIF has good reconstruction quality but high computational cost. The state-of-the-art IAIF method is computationally fast but its accuracy deteriorates, particularly for speech signals of high fundamental frequency (F0). The results show the competitive performance of the new method: With high F0, the reconstruction quality is better than that of IAIF and close to MCMC-GIF while reducing the computational complexity by two orders of magnitude. en
dc.format.extent 19
dc.language.iso eng
dc.relation.ispartof Inverse Problems
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject ill-posed problems
dc.subject glottal inverse filtering
dc.subject double regularisation
dc.subject alternating minimisation
dc.subject glottal airflow
dc.subject wavelets
dc.subject deterministic
dc.subject VOCAL-TRACT
dc.subject SCATTERING
dc.subject SHAPE
dc.subject QUALITY
dc.subject MODEL
dc.subject 111 Mathematics
dc.subject 114 Physical sciences
dc.title Alternating minimisation for glottal inverse filtering en
dc.type Article
dc.contributor.organization Department of Mathematics and Statistics
dc.contributor.organization Mikko Samuli Siltanen / Principal Investigator
dc.description.reviewstatus Peer reviewed
dc.relation.issn 0266-5611
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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