Alternating minimisation for glottal inverse filtering. (17th May 2017)

• Record Type: Journal Article
• Authors: Bleyer, Ismael Rodrigo; Lybeck, Lasse; Auvinen, Harri; Airaksinen, Manu; Alku, Paavo; Siltanen, Samuli
• Is Part Of: Inverse problems. Volume 33:Number 6(2017:Jun.)
• Publication Date: 2017-05-17
• Subjects: ill-posed problems -- glottal inverse filtering -- double regularisation -- alternating minimisation -- glottal airflow -- wavelets -- deterministic; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/1361-6420/aa6eb8
• Languages: English
• ISSNs: 0266-5611

Abstract

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 ( F 0). The results show the competitive performance of the new method: With high F 0, the reconstruction quality is better than that of IAIF and close to MCMC-GIF while reducing the computational complexity by two orders of magnitude.