Abstract
The sinusoidal model has proven useful for representation and modification of speech and audio signal. One drawback, however, is that a sinusoidal model is typically derived using a fixed analysis frame size. It cannot guarantee an optimal spectral resolution to each sinusoidal parameter. In this paper, we propose a sinusoidal model using wavelet packet analysis, to obtain better frequency resolution at low frequencies and better time resolution at high frequencies and to estimate the sinusoidal parameters more accurately. Experiments show that the proposed model can achieve better performance than conventional model.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 241-248 |
| Number of pages | 8 |
| Volume | 3658 LNAI |
| Publication status | Published - 2005 Dec 1 |
| Event | 8th International Conference on Text, Speech and Dialogue, TSD 2005 - Karlovy Vary, Czech Republic Duration: 2005 Sep 12 → 2005 Sep 15 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 3658 LNAI |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 8th International Conference on Text, Speech and Dialogue, TSD 2005 |
|---|---|
| Country | Czech Republic |
| City | Karlovy Vary |
| Period | 05/9/12 → 05/9/15 |
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ASJC Scopus subject areas
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science
Cite this
Sinusoidal modeling using wavelet packet transform applied to the analysis and synthesis of speech signals. / Kim, Kihong; Hong, Jinkeun; Lim, Jong In.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3658 LNAI 2005. p. 241-248 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3658 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Sinusoidal modeling using wavelet packet transform applied to the analysis and synthesis of speech signals
AU - Kim, Kihong
AU - Hong, Jinkeun
AU - Lim, Jong In
PY - 2005/12/1
Y1 - 2005/12/1
N2 - The sinusoidal model has proven useful for representation and modification of speech and audio signal. One drawback, however, is that a sinusoidal model is typically derived using a fixed analysis frame size. It cannot guarantee an optimal spectral resolution to each sinusoidal parameter. In this paper, we propose a sinusoidal model using wavelet packet analysis, to obtain better frequency resolution at low frequencies and better time resolution at high frequencies and to estimate the sinusoidal parameters more accurately. Experiments show that the proposed model can achieve better performance than conventional model.
AB - The sinusoidal model has proven useful for representation and modification of speech and audio signal. One drawback, however, is that a sinusoidal model is typically derived using a fixed analysis frame size. It cannot guarantee an optimal spectral resolution to each sinusoidal parameter. In this paper, we propose a sinusoidal model using wavelet packet analysis, to obtain better frequency resolution at low frequencies and better time resolution at high frequencies and to estimate the sinusoidal parameters more accurately. Experiments show that the proposed model can achieve better performance than conventional model.
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M3 - Conference contribution
AN - SCOPUS:33646030635
SN - 3540287892
SN - 9783540287896
VL - 3658 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 241
EP - 248
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -