A novel pipelined neural IIR adaptive filter for speech prediction. (1st December 2018)

• Record Type: Journal Article
• Authors: Yan, Wei; Zhang, Jiashu; Zhang, Sheng; Wen, Pengwei
• Is Part Of: Applied acoustics. Volume 141(2018)
• Page Start: 64
• Page End: 70
• Publication Date: 2018-12-01
• Subjects: Nonlinear speech prediction -- Neural IIR filter -- Pipelined architecture; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2018.06.007
• Languages: English
• ISSNs: 0003-682X

Abstract

Abstract: The paper presents a pipelined neural IIR filter (PNIIR) for nonlinear speech prediction. It inherits the usual pipelined two layers architecture: the nonlinear modular cascaded subsections and linear combiner subsection, and the nonlinear and linear weights of each module are updated using an real-time learning algorithms. The PNIIR filter units the good tracking performance of the neural IIR network and the low computation load of the pipelined architecture. The performance analysis and complexity analysis are illustrated in this paper. The experimental study for speech prediction is also carried out to testify the efficiency of the proposed nonlinear filter.