Parametric Approximation of Piano Sound Based on Kautz Model with Sparse Linear Prediction

Kenji Kobayashi, Daiki Takeuchi, Mio Iwamoto, Kohei Yatabe, Yasuhiro Oikawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

The piano is one of the most popular and attractive musical instruments that leads to a lot of research on it. To synthesize the piano sound in a computer, many modeling methods have been proposed from full physical models to approximated models. The focus of this paper is on the latter, approximating piano sound by an IIR filter. For stably estimating parameters, the Kautz model is chosen as the filter structure. Then, the selection of poles and excitation signal rises as the questions which are typical to the Kautz model that must be solved. In this paper, sparsity based construction of the Kautz model is proposed for approximating piano sound.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages626-630
Number of pages5
ISBN (Print)9781538646588
DOIs
Publication statusPublished - 2018 Sep 10
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 2018 Apr 152018 Apr 20

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/TerritoryCanada
CityCalgary
Period18/4/1518/4/20

Keywords

  • Autoregressive (AR) spectrum estimation
  • Difference of convex (DC) algorithm
  • IIR filter design
  • Sparse optimization
  • ℓ constrained least squares

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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