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

*この研究の対応する著者

研究成果

13 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing
出版社Institute of Electrical and Electronics Engineers Inc.
ページI237-I240
ISBN(印刷版)0780388747, 9780780388741
DOI
出版ステータスPublished - 2005
外部発表はい
イベント2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
継続期間: 2005 3 182005 3 23

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
I
ISSN(印刷版)1520-6149

Conference

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

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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