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• W3128624951 abstract "In this paper, we try to classify the emotional cues of sound and visual stimuli solely from their source characteristics, i.e., from the 1D time series generated from the audio signals and the two-dimensional matrix of pixels generated from the affective picture stimulus. The sample data consists of six audio signals of 15 s each and six affective pictures, of which three belonged to positive and negative valence, respectively. Detrended Fluctuation Analysis (DFA) has been used to calculate the long-range temporal correlations or the Hurst exponent corresponding to the audio signals. The 2D analogue of the DFA technique has been applied on the array of pixels corresponding to affective pictures of contrast emotions. We obtain a single unique scaling exponent corresponding to each audio signal and three scaling exponents corresponding to red/green/blue (RGB) component in each of the visual images. Detrended Cross-correlation (DCCA) technique (both 1D and 2D) has been used to calculate the degree of nonlinear correlation present between the sample audio and visual clips. To assess the proportion of cross-modal correlation in the emotional appraisal, Pearson correlation coefficient was calculated using the DFA exponents of the two modalities. The results and findings have been corroborated with a human response study based on the emotional Likert scale ratings. To conclude, we propose a novel algorithm with which emotional arousal can be classified in cross-modal scenario using only the source audio and visual signals while also attempting to assess the degree of correlation between them." @default.
• W3128624951 created "2021-02-15" @default.
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• W3128624951 date "2023-01-01" @default.
• W3128624951 modified "2023-10-18" @default.
• W3128624951 title "A Fractal Approach to Characterize Emotions in Audio and Visual Domain: A Study on Cross-Modal Interaction" @default.
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• W3128624951 doi "https://doi.org/10.1007/978-3-031-18444-4_20" @default.
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