An auto-regressive, non-stationary excited signal parameter estimation method and an evaluation of a singing-voice recognition

Akira Sasou, Masataka Goto, Satoru Hayamizu, Kazuyo Tanaka

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

12 Citations (Scopus)

Abstract

We have previously described an Auto-Regressive Hidden Markov Model (AR-HMM) and an accompanying parameter estimation method. The AR-HMM was obtained by combining an AR process with an HMM introduced as a non-stationary excitation model. We demonstrated that the AR-HMM can accurately estimate the characteristics of both articulatory systems and excitation signals from high-pitched speech. In this paper, we apply the AR-HMM to feature extraction from singing voices and evaluate the recognition accuracy of the AR-HMM-based approach.

Original languageEnglish
Title of host publication2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
PagesI237-I240
ISBN (Print)0780388747, 9780780388741
DOIs
Publication statusPublished - 2005
Externally publishedYes
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: 2005 Mar 182005 Mar 23

Publication series

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

Conference

Conference2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
CountryUnited States
CityPhiladelphia, PA
Period05/3/1805/3/23

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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