Comparison of auto-regressive, non-stationary excited signal parameter estimation methods

Akira Sasou; Masataka Goto; Satoru Hayamizu; Kazuyo Tanaka

Comparison of auto-regressive, non-stationary excited signal parameter estimation methods

Akira Sasou, Masataka Goto, Satoru Hayamizu, Kazuyo Tanaka

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Previously, we proposed an Auto-Regressive Hidden Markov Model (AR-HMM) and an accompanying parameter estimation method. An 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. As the parameter estimation method iteratively executes learning processes of HMM parameters, the proposed method was calculation-intensive. Here, we propose two novel kinds of auto-regressive, non-stationary excited signal parameter estimation methods to reduce the amount of calculation required.

Original language	English
Title of host publication	Machine Learning for Signal Processing XIV - Proceedings of 2004 IEEE Signal Processing Society Workshop
Editors	A. Barros, J. Principe, J. Larsen, T. Adali, S. Douglas
Pages	295-304
Number of pages	10
Publication status	Published - 2004
Externally published	Yes
Event	Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop - Sao Luis, Brazil Duration: 2004 Sept 29 → 2004 Oct 1

Publication series

Name	Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop

Conference

Conference	Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop
Country/Territory	Brazil
City	Sao Luis
Period	04/9/29 → 04/10/1

ASJC Scopus subject areas

Engineering(all)

Cite this

Sasou, A., Goto, M., Hayamizu, S., & Tanaka, K. (2004). Comparison of auto-regressive, non-stationary excited signal parameter estimation methods. In A. Barros, J. Principe, J. Larsen, T. Adali, & S. Douglas (Eds.), Machine Learning for Signal Processing XIV - Proceedings of 2004 IEEE Signal Processing Society Workshop (pp. 295-304). (Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop).

Comparison of auto-regressive, non-stationary excited signal parameter estimation methods. / Sasou, Akira; Goto, Masataka; Hayamizu, Satoru et al.
Machine Learning for Signal Processing XIV - Proceedings of 2004 IEEE Signal Processing Society Workshop. ed. / A. Barros; J. Principe; J. Larsen; T. Adali; S. Douglas. 2004. p. 295-304 (Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sasou, A, Goto, M, Hayamizu, S & Tanaka, K 2004, Comparison of auto-regressive, non-stationary excited signal parameter estimation methods. in A Barros, J Principe, J Larsen, T Adali & S Douglas (eds), Machine Learning for Signal Processing XIV - Proceedings of 2004 IEEE Signal Processing Society Workshop. Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop, pp. 295-304, Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop, Sao Luis, Brazil, 04/9/29.

Sasou A, Goto M, Hayamizu S, Tanaka K. Comparison of auto-regressive, non-stationary excited signal parameter estimation methods. In Barros A, Principe J, Larsen J, Adali T, Douglas S, editors, Machine Learning for Signal Processing XIV - Proceedings of 2004 IEEE Signal Processing Society Workshop. 2004. p. 295-304. (Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop).

Sasou, Akira ; Goto, Masataka ; Hayamizu, Satoru et al. / Comparison of auto-regressive, non-stationary excited signal parameter estimation methods. Machine Learning for Signal Processing XIV - Proceedings of 2004 IEEE Signal Processing Society Workshop. editor / A. Barros ; J. Principe ; J. Larsen ; T. Adali ; S. Douglas. 2004. pp. 295-304 (Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop).

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Comparison of auto-regressive, non-stationary excited signal parameter estimation methods

Abstract

Publication series

Conference

ASJC Scopus subject areas

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