Literature DB >> 31795672

Mel-frequency cepstral coefficients derived using the zero-time windowing spectrum for classification of phonation types in singing.

Sudarsana Reddy Kadiri1, Paavo Alku1.   

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.

Year:  2019        PMID: 31795672     DOI: 10.1121/1.5131043

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  1 in total

1.  Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning.

Authors:  Zhongkui Xu
Journal:  Front Psychol       Date:  2022-05-26
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.