Acoustic Modeling for Emotion Recognition / by Koteswara Rao Anne, Swarna Kuchibhotla, Hima Deepthi Vankayalapati.
By: Anne, Koteswara Rao., autor.
Contributor(s): Kuchibhotla, Swarna., autor. | Vankayalapati, Hima Deepthi., autor.
Material type:
E-bookSeries: (SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,, 2191-737X); (Engineering (Springer-11647)).Publisher: Cham : Springer International Publishing, 2015Description: 1 recurso en línea (VII, 66 páginas 24 ilustraciones, 17 ilustraciones a color.).ISBN: 9783319155302.Subject: Acústica
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|
LIBRO-E NO PRÉSTAMO
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | QC243 2015 EB (Browse shelf(Opens below)) | Acceso electrónico | eBook.12112712 |
Browsing Madrid Digital shelves, Shelving location: Acceso Electrónico (UEM) Close shelf browser (Hides shelf browser)
| QC228.2 2009 EB Multi-Pitch Estimation | QC242.2 2019 EB Underwater Acoustic Signal Processing : Modeling, Detection, and Estimation | QC242.2 2022 EB Underwater Acoustic Channel | QC243 2015 EB Acoustic Modeling for Emotion Recognition | QC243 2018 EB Computational Acoustics | QC243 2021 EB Time Reversal Acoustics | QC243 2022 EB Sound Synthesis, Propagation, and Rendering |
Introduction -- Emotion Recognition using Prosodic features -- Emotion Recognition using Spectral features -- Emotional Speech Corpora -- Classification Models -- Comparative Analysis of Classifiers in emotion recognition -- Summary and Conclusions.
This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications - gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.
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