FF-BTP Model for Novel Sound-Based Community Emotion Detection
Article
Yildiz, Arif Metehan, Tanabe, Masayuki, Kobayashi, Makiko, Tuncer, Ilknur, Barua, Prabal Datta, Dogan, Sengul, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra. 2023. "FF-BTP Model for Novel Sound-Based Community Emotion Detection." IEEE Access. 11, pp. 108705-108715. https://doi.org/10.1109/ACCESS.2023.3318751
| Article Title | FF-BTP Model for Novel Sound-Based Community Emotion Detection |
|---|---|
| ERA Journal ID | 210567 |
| Article Category | Article |
| Authors | Yildiz, Arif Metehan, Tanabe, Masayuki, Kobayashi, Makiko, Tuncer, Ilknur, Barua, Prabal Datta, Dogan, Sengul, Tuncer, Turker, Tan, Ru-San and Acharya, U. Rajendra |
| Journal Title | IEEE Access |
| Journal Citation | 11, pp. 108705-108715 |
| Number of Pages | 11 |
| Year | 2023 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | United States |
| ISSN | 2169-3536 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2023.3318751 |
| Web Address (URL) | https://ieeexplore.ieee.org/abstract/document/10262006 |
| Abstract | Most emotion classification schemes to date have concentrated on individual inputs rather than crowd-level signals. In addressing this gap, we introduce Sound-based Community Emotion Recognition (SCED) as a fresh challenge in the machine learning domain. In this pursuit, we crafted the FF-BTP-based feature engineering model inspired by deep learning principles, specifically designed for discerning crowd sentiments. Our unique dataset was derived from 187 YouTube videos, summing up to 2733 segments each of 3 seconds (sampled at 44.1 KHz). These segments, capturing overlapping speech, ambient sounds, and more, were meticulously categorized into negative, neutral, and positive emotional content. Our architectural design fuses the BTP, a textural feature extractor, and an innovative handcrafted feature selector inspired by Hinton's FF algorithm. This combination identifies the most salient feature vector using calculated mean square error. Further enhancements include the incorporation of a multilevel discrete wavelet transform for spatial and frequency domain feature extraction, and a sophisticated iterative neighborhood component analysis for feature selection, eventually employing a support vector machine for classification. On testing, our FF-BTP model showcased an impressive 97.22% classification accuracy across three categories using the SCED dataset. This handcrafted approach, although inspired by deep learning's feature analysis depth, requires significantly lower computational resources and still delivers outstanding results. It holds promise for future SCED-centric applications. © 2013 IEEE. |
| Keywords | FF-BTP; sound community emotion classification; sound processing; textural feature extraction |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | Firat University, Turkey |
| Kumamoto University, Japan | |
| School of Business | |
| Duke-NUS Medical School, Singapore | |
| School of Mathematics, Physics and Computing |
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