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Abstract
Existing studies in classification of phonation types in singing use voice source features and Mel-frequency cepstral coefficients (MFCCs) showing poor performance due to high pitch in singing. In this study, high-resolution spectra obtained using the zero-time windowing (ZTW) method is utilized to capture the effect of voice excitation. ZTW does not call for computing the source-filter decomposition (which is needed by many voice source features) which makes it robust to high pitch. For the classification, the study proposes extracting MFCCs from the ZTW spectrum. The results show that the proposed features give a clear improvement in classification accuracy compared to the existing features.
Original language | English |
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Pages (from-to) | EL418-EL423 |
Journal | Journal of the Acoustical Society of America |
Volume | 146 |
Issue number | 5 |
DOIs | |
Publication status | Published - 8 Nov 2019 |
MoE publication type | A1 Journal article-refereed |
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Dive into the research topics of 'Mel-frequency cepstral coefficients derived using the zero-time windowing spectrum for classification of phonation types in singing'. Together they form a unique fingerprint.Projects
- 1 Finished
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Interdisciplinary research on statistical parametric speech synthesis
Alku, P. (Principal investigator), Bäckström, T. (Project Member), Juvela, L. (Project Member), Murtola, T. (Project Member), Nonavinakere Prabhakera, N. (Project Member), Bollepalli, B. (Project Member) & Airaksinen, M. (Project Member)
01/01/2018 → 31/12/2019
Project: Academy of Finland: Other research funding